I.                MAIN COMPETENCY COURSES (MAC)

UIN6021204 Arabic

Module Name	Arabic Literature
Module level, if applicable	Undergraduate
Module Identification Code	UIN6021204
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Dr. Achmad Fudhaili M.Pd
Language	Indonesian and Arabic
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Lecture, class discussion, structured activities (homework, quizzes), Collaborative Learning .
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 70.00 Lecture (ECTS) : 3.67 Practical (ECTS) : 0 Total ECTS : 3.667
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Developed a fundamental understanding of the Arabic language, including grammar, vocabulary, and pronunciation. Gained proficiency in reading and comprehending simple Arabic texts and expressions. Acquired basic conversational skills to engage in everyday discussions and interactions in Arabic. Expanded their vocabulary and functional language use for various common situations in Arabic. Enhanced their listening skills to grasp and interpret spoken Arabic in various contexts. Improved their writing skills to construct simple paragraphs and express ideas coherently in Arabic. Demonstrated cultural sensitivity and awareness when using Arabic in diverse social and cultural settings. Acquired foundational knowledge of Arab culture, traditions, and societal norms related to language use. Exhibited the ability to introduce themselves and others in Arabic, and provide basic personal information. Shown proficiency in using Arabic for common activities like shopping, ordering food, giving directions, etc. Mastered the Arabic script and its application in reading and writing. Demonstrated the capability to describe people, places, and events in Arabic. Gained insights into the interconnectedness of language and culture in Arabic-speaking communities. Displayed readiness to further advance their Arabic language skills and pursue higher levels of proficiency. Successfully applied the learned language skills to practical situations, enhancing their overall Arabic language competence.
Module content
Introduction to Arabic Language and Culture Arabic Alphabet and Pronunciation Basic Arabic Vocabulary and Expressions Grammar Fundamentals Arabic Reading and Comprehension Arabic Writing Practice Conversational Arabic Arabic Vocabulary Expansion Listening and Speaking Proficiency Cultural Etiquette and Practices Intermediate Grammar and Sentence Structure Reading Comprehension and Analysis Expressing Opinions and Descriptions Role of Arabic in the Modern World Final Project and Presentation
Reference: Mastering Arabic Script: A Guide to Handwriting” by Jane Wightwick and Mahmoud Gaafar (2019) Arabic Language and Culture Through Art” by Nasser Isleem and Ghazi Abuhakema (2021) Practice Makes Perfect: Arabic Verb Tenses, 2nd Edition” by Jane Wightwick and Mahmoud Gaafar (2020) Alif Baa: Introduction to Arabic Letters and Sounds, 4th Edition” by Kristen Brustad, Mahmoud Al-Batal, and Abbas Al-Tonsi (2021) Ahlan wa Sahlan: Functional Modern Standard Arabic for Beginners, 3rd Edition” by Mahdi Alosh (2020) Arabic Stories for Language Learners: Traditional Middle Eastern Tales in Arabic and English” by Hezi Brosh and Lutfi Mansur (2020) Modern Standard Arabic Grammar: A Learner’s Guide” by Mohammad T. Alhawary (2021) Arabic: An Essential Grammar, 2nd Edition” by Faruk Abu-Chacra (2021) Developing Writing Skills in Arabic” by Taoufik Ben Amor (2021) The Connectors in Modern Standard Arabic” by Erwin Wendling (2019)

FST609116 Algorithms and Programming

Module Name	Basic Computer Science and Programming
Module level, if applicable	Undergraduate
Module Identification Code	FST6091102
Semester(s) in which the module is taught	1
Person(s) responsible for the module	Muhaza Liebenlito, M.Si.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-Based Learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignments): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Capable of determining a data type and structuring programming logic, as well as implementing simple computational problems into basic programming using Python and R.
Module content
Basic concepts of computer science and programming Branching and iteration String manipulation Array concepts in Python and R Functions in Python and R Introduction to libraries Working with files Data visualization Debugging, exceptions, and assertions Object-oriented programming concepts
Reference: Matthes, E. (2023). Python crash course: A hands-on, project-based introduction to programming (3rd ed.). No Starch Press. Sweigart, A. (2019). Automate the boring stuff with Python (2nd ed.). No Starch Press. Matthes, M. (2023). Python programming for beginners: An introduction to the basics of Python programming. Independently Published. Wickham, H., & Grolemund, G. (2021). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media. Severance, C. (2023). Python for everybody: Exploring data in Python 3. CreateSpace Independent Publishing Platform. Guttag, J. V. (2021). Introduction to computation and programming using Python (3rd ed.). The MIT Press. Lutz, M. (2023). Learning Python (6th ed.). O’Reilly Media. Grolemund, G. (2020). Hands-on programming with R: Write your own functions and simulations. O’Reilly Media. Downey, A. B. (2022). Think Python: How to think like a computer scientist (2nd ed.). Green Tea Press. Metzler, N. (2022). R programming for beginners: Statistical programming and data analysis. Independently Published.

FST6032202 Islam and Science

Module Name	Islam and Science
Module level, if applicable	Undergraduate
Module Identification Code	FST6032202
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Fardiana Fikria Qur’any, M. Ud
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 56.00 Lecture (ECTS) : 3.20 Practical (ECTS) : 0 Total ECTS : 3.200
Credit points	3 Credit Hours ≈ 3.200 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignments): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, Students can identify and discuss the relationships between science, philosophy, and religion, understand the history of the development of science, and the integration of knowledge from classical to modern times. The study includes aspects of research and discoveries in the field of science related to themes such as human beings, technology, health, social psychology, culture, politics, economics, and so on.
Module content
Introduction to Science, Philosophy, and Religion RelationshipsHistorical Development of ScienceIntegration of Knowledge from Classical to Modern TimesResearch and Discoveries in Various Scientific ThemesScience and Human BeingsScience and TechnologyScience and HealthScience and Social PsychologyScience and CultureScience and PoliticsScience and EconomicsIntegration and Interdisciplinary AspectsCritical Analysis and DebatesFuture Trends and Implications
Reference: Ahmad, K. (2020). Islam and science: An intellectual reappraisal. Islamic Book Trust. Nasr, S. H. (2021). Science and civilization in Islam (New ed.). Harvard University Press. Dhanani, A. (2018). The physical world in the Islamic thought: Essential readings in classical and modern texts. Brill. Lumbard, J. E. B. (2022). Islamic science and the making of the European Renaissance. Harvard University Press. Alatas, S. F. (2019). Applying Ibn Khaldun: The recovery of a lost tradition in sociology. Routledge. Ashworth, W. J., & Elshakry, M. T. (2021). Islamic cosmopolitanism: History, science, and culture. Oxford University Press. Daiber, H. (2020). Knowledge and science in classical Islam: Religious and philosophical foundations. Brill. Osman, A. (2018). Islam and science: The linkages between religion and modern scientific thought. I.B. Tauris. Mozaffari, M. (2019). Science and religion in Islam: The life of reason in Islamic thought. Cambridge University Press. Saliba, G. (2021). Islamic science and the scientific revolution: The legacy of medieval Arab-Islamic science. MIT Press

FST6094101 Calculus I

Module Name	Calculus I
Module level, if applicable	Undergraduate
Module Identification Code	FST6094101
Semester(s) in which the module is taught	1
Person(s) responsible for the module	Yanne Irene
Language	Indonesian
Relation in Curriculum	Compulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.51 Practical (ECTS) : 0 Total ECTS : 5.511
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Student should be proficient in elementary algebra
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: ability to solve problems related to some properties of real numbers  and functions. ability to solve problems on limits, continuity, derivatives, and  geometric interpretation of derivatives. ability to apply derivatives in solving problems related to limits, extreme value, and sketching a graph of a function ability to solve problems on integral and its application ability to solve problems on transcedental functions
Module content
System of real numbers Functions and their graph The limit of a function The derivatives , the geometric  interpretation of the derivatives, higher-order derivativesExtreme value problem, applications of  extreme problem, increasing and decreasing functions, concavity, inflection points, sketching the graph of functionsDefinite IntegralApplication of IntegralTranscedental Functions
Reference: Anton, H., Bivens, I., & Davis, S. (2020). Calculus: Early transcendentals (12th ed.). Wiley. Stewart, J. (2021). Calculus: Concepts and contexts (9th ed.). Cengage Learning. Rogawski, J., & Adams, C. (2019). Calculus: Early transcendentals (4th ed.). W.H. Freeman and Company. Strang, G., & Herman, E. (2020). Calculus volume 1 (Open Access Textbook). OpenStax. Hass, J., Heil, C., & Weir, M. D. (2020). Thomas’ calculus: Early transcendentals (15th ed.). Pearson. Briggs, W. L., Cochran, L., & Gillett, B. (2022). Calculus: Early transcendentals (4th ed.). Pearson. Simmons, G. F., & Krantz, S. G. (2020). Calculus with analytic geometry (2nd ed.). McGraw Hill. Smith, R. T., & Minton, R. B. (2022). Calculus: Early transcendentals (4th ed.). McGraw Hill. Strang, G. (2019). Calculus (Revised ed.). Wellesley-Cambridge Press. Marsden, J. E., & Tromba, A. J. (2020). Vector calculus (6th ed.). W.H. Freeman and Company.

FST6094103 Discrete Mathematics

Module Name	Discrete Mathematics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094103
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Yanne Irene
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	4 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Ability to understand mathematical reasoning in order to read, comprehend, and construct mathematical arguments. Ability to work with discrete structure, such as sets, permutation, relations. Ability to identify combinatorial problems and ability to solve using appropriate principles of combinatorics. Ability to solve problems in discrete probability. Ability to solve some linear recurrence relations Ability to prove the properties of lattice and Boolean algebra
Module content
Logic and Proof Basic Structure: Sets, Functions, sequences, Sums, and Matrices Number Theory and Cryptography Induction and Recursion Counting Dicrete Probability Relations Boolean Algebra
Reference: Epp, S. S. (2020). Discrete mathematics with applications (5th ed.). Cengage Learning. Rosen, K. H. (2019). Discrete mathematics and its applications (8th ed.). McGraw Hill. Goodaire, E. G., & Parmenter, M. M. (2021). Discrete mathematics with graph theory (4th ed.). Pearson. Johnsonbaugh, R. (2022). Discrete mathematics (8th ed.). Pearson. Scheinerman, E. R. (2019). Mathematics: A discrete introduction (4th ed.). Cengage Learning. Dossey, J. A., Otto, A. D., Spence, L. E., & Vanden Eynden, C. (2020). Discrete mathematics (6th ed.). Pearson. Grimaldi, R. P. (2020). Discrete and combinatorial mathematics: An applied introduction (6th ed.). Pearson. Biggs, N. (2021). Discrete mathematics (2nd ed.). Oxford University Press. Hunter, D. J. (2020). Essentials of discrete mathematics (4th ed.). Jones & Bartlett Learning. Anderson, I. (2022). Discrete mathematics with combinatorics (3rd ed.). Springer.

FST6091110 Digital Literacy

Module Name	Digital Literacy
Module level, if applicable	Undergraduate
Module Identification Code	FST6091101
Semester(s) in which the module is taught	1
Person(s) responsible for the module	Mohamad Irvan Septiar Musti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU): 2 Number of lecture per Semester: 14 Practical (at Laboratory or filed) (SCU): Number of Practical Per Semester: Total Hours Lecture (Face to Face) Per Semester: 23.33 Hours of Midterm And Final Exam Per Semester: 3.33 Total Hours Practical: 0.00 Total Hours of Structure and Self Study Per semester: 37.33 Lecture (ECTS): 2.13 Practical (ECTS): 0 Total ECTS: 2.133
Credit points	4 Credit Hours ≈ 2.13 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are able to understand the history, role, and benefits of Information and Communication Technology (ICT). Students are able to explain an overview of computer systems. Students are able to explain the concepts and tasks of operating systems. Students are able to explain the history of Unix, Linux, and Windows operating systems. Students can explain the definition, benefits, and workings of computer networks and the internet. Students are able to explain the processes that occur at the OSI Layer. Students are able to explain the types of IP Addresses and how they work. Students can understand the development of computing and cloud computing. Students are able to explain the architecture, storage media, and security mechanisms in cloud computing. Students have the ability to describe various types of databases and provide explanations regarding the benefits of databases. Additionally, students can identify the uses and practical applications of databases in various industries and sectors. Students have the ability to describe and understand the fundamental concepts of the Data Ecosystem, encompassing various important aspects of data management. Students have the ability to comprehensively explain programming languages. They understand the definition and purpose of programming languages and also comprehend the significant role of programming languages in software development. Students have the ability to comprehensively describe various aspects of cybercrime. They understand the definition of cybercrime, referring to illegal or harmful activities conducted online, including attacks and violations of computer systems and networks.
Module content
Introduction: The History of the Development of Information and Communication Technology (ICT), Computer Systems, Operating Systems, Computer Networks, and the Internet. Reference Model (OSI Layer), Fundamentals of IP Addresses, Cloud Computing Systems, Architecture, Security Mechanisms, and Storage Media in Cloud Computing. Fundamentals of Databases, Data Ecosystem, Programming Languages, and Cybercrime and Cybersecurity.
Reference: Andrews, J., & Dark, J. (2023). A+ guide to IT technical support (11th ed.). Cengage Learning. Cloud, S. (2022). Understanding cloud computing: Architecture, storage, and security. Wiley. Comer, D. E. (2021). Computer networks and internets (7th ed.). Pearson. Fitzgerald, J., Dennis, A., & Durcikova, A. (2020). Business data communications and networking (14th ed.). Wiley. Forouzan, B. A. (2021). Data communications and networking (6th ed.). McGraw Hill. Kurose, J. F., & Ross, K. W. (2021). Computer networking: A top-down approach (8th ed.). Pearson. Le, D., & Varanasi, K. (2023). Cybersecurity essentials for IT professionals. Springer. Panko, R. R., & Panko, J. P. (2020). Business data networks and security (11th ed.). Pearson. Sebesta, R. W. (2022). Concepts of programming languages (13th ed.). Pearson. Stallings, W. (2021). Operating systems: Internals and design principles (10th ed.). Pearson.

UIN6032201 Islamic Studies

Module Name	Islamic Studies
Module level, if applicable	Undergraduate
Module Identification Code	UIN6032201
Semester(s) in which the module is taught	1
Person(s) responsible for the module	Dr. Syamsul Aripin. MA.
Language	Indonesian
Relation in Curriculum	Compulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU): 4 Number of lecture per Semester: 14 Practical (at Laboratory or filed) (SCU):   Number of Practical Per Semester:  Total Hours Lecture (Face to Face) Per Semester: 46.67 Hours of Midterm And Final Exam Per Semester: 6.67 Total Hours Practical: 0.00 Total Hours of Structure and Self Study Per semester: 74.67 Lecture (ECTS): 4.27 Practical (ECTS): 0 Total ECTS: 4.267
Credit points	4 Credit Hours ≈ 4.267 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Student should be proficient in elementary algebra
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are proficient in understanding the definition, origins, types, elements, and functions of religion for human life based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments. Students are proficient in understanding the definition of Islam, its characteristics, similarities, and differences with other religions, as well as the sources and fundamental teachings of Islam based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments. Students are proficient in understanding aspects of Islamic teachings related to worship, spiritual and moral exercises, history and culture of Islam, politics, education, preaching, community and gender equality in Islam based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments.
Module content
Definition, Origins, Types, Elements, Purpose, and Function of Religion.Human Needs for Religion.Islam in its True Sense.Characteristics and Principles of Islamic Teachings, Similarities and Differences with Other Religions.Essential Principles of Islam: Faith, Islam, and Ihsan/Faith, Knowledge, and Deeds.Aspects of Worship, Spiritual Exercises, and Moral Teachings in Islam.Aspects of History and Culture of Islam.Political and Institutional Aspects of Islam.Educational Aspects in Islam.Aspects of Islamic Preaching (Dakwah).Community Aspects in Islam.Aspects of Moral Development in Islam.
Reference: Ahmad, K. (2022). Understanding religion and human life: Perspectives from Islam and other faiths. Routledge. Al-Ghazali. (2020). The revival of religious sciences (Ihya’ Ulum al-Din) (F. Karim, Trans.). Islamic Texts Society. Asad, M. (2021). The principles of Islam and their relevance today. Islamic Book Trust. Esposito, J. L. (2020). Islam: The straight path (5th ed.). Oxford University Press. Hallaq, W. B. (2022). Shari‘a: Theory, practice, and transformations. Cambridge University Press. Kamali, M. H. (2021). Shari’ah law: An introduction (3rd ed.). Oneworld Publications. Nasr, S. H. (2021). Islam and the perennial philosophy: History and culture of Islamic thought. HarperOne. Ramadan, T. (2020). The essentials of Islam: A guide to faith and practice. Oxford University Press. Saeed, A. (2022). Islam in modern society: Faith, values, and practice. Bloomsbury Academic. Zain, M. M. (2023). Comprehensive Islamic teachings: Moral, social, and spiritual insights. Islamic Research Publications.

FST6094105 Elementary Linear Algebra

Module Name	Elementary Linear Algebra
Module level, if applicable	Undergraduate
Module Identification Code	FST6094105
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 130.67 Lecture (ECTS) : 6.13 Practical (ECTS) : 0 Total ECTS : 6.133
Credit points	4 Credit Hours ≈ 6.133 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Student should be proficient in Calculus I and Discrete Mathematics
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, Students are able to solve problems (C4) related to linear equations and matrices, and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to determinants and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to Euclidean vector spaces and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to general vector spaces and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to eigenvalues and eigenvectors and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to dot products and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to diagonalization and quadratic forms, and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to linear transformations and present the results (A5) using both oral and written language.
Module content
Systems of Linear Equations and Matrices, Determinants, Euclidean Vector Spaces, General Vector Spaces, Eigenvalues and Eigenvectors, Dot Products, Diagonalization and Quadratic Forms, Linear Transformations.
Reference: Anton, H., & Kaul, C. (2022). Elementary linear algebra (12th ed.). Wiley. Poole, D. (2020). Linear algebra: A modern introduction (4th ed.). Cengage Learning. Kolman, B., & Hill, D. R. (2020). Introductory linear algebra with applications (11th ed.). Pearson. Friedberg, S. H., Insel, A. J., & Spence, L. E. (2021). Linear algebra (5th ed.). Pearson. Hefferon, J. (2021). Linear algebra: A geometric approach (2nd ed.). Dover Publications. Lay, D. C. (2020). Linear algebra and its applications (5th ed.). Pearson. Axler, S. (2020). Linear algebra done right (3rd ed.). Springer. Strang, G. (2021). Introduction to linear algebra (5th ed.). Wellesley-Cambridge Press. Nicholson, W. K. (2020). Linear algebra with applications (8th ed.). Pearson. McDonald, J. F., & Watrous, J. P. (2021). Elementary linear algebra with applications (2nd ed.). John Wiley & Sons.

NAS6013203 Indonesian Literature

Module Name	Indonesian
Module level, if applicable	Undergraduate
Module Identification Code	NAS6013203
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Didah Nurhamidah, M.Pd
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 56.00 Lecture (ECTS) : 3.20 Practical (ECTS) : 0 Total ECTS : 3.200
Credit points	3 Credit Hours ≈ 3.200 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Speaking Skills in Academic Presentation: Students are able to speak in scientific presentations.Understanding the Development of the Indonesian Language: Students can understand the development of the Indonesian language.Understanding the Use of Letters and Words: Students can understand the use of letters and words.Understanding Borrowed Words and Punctuation: Students can understand borrowed words and punctuation.Proper Diction Usage: Students are able to use appropriate diction.Crafting Effective Sentences: Students are able to create effective sentences.Constructing Proper Paragraphs: Students are able to create proper paragraphs.Understanding Plagiarism: Students understand plagiarism.Essay Planning Abilities: Students are able to plan an essay.Effective Reasoning Skills: Students are able to reason accurately.Utilizing Scientific Notation Efficiently: Students are able to use scientific notation efficiently.Producing Short Writings Correctly: Students are able to produce short writings correctly.Reproduction of Writing Accurately: Students are able to reproduce writings accurately.
Module content
Speaking in Scientific Presentations;Development of the Indonesian Language;Usage of Letters and Words;Borrowed Elements, Punctuation, and Transliteration;Diction/Word Choice;Effective Sentences;Paragraphs;Scientific Ethics/Plagiarism;Essay Planning;Reasoning;Scientific Notation;Short Writing Production;Writing Reproduction.
Reference: Paramaditha, I. (2020). The wandering. Gramedia Pustaka Utama. Lestari, D. (2017). Paper boats. Penerbit Buku Kompas. Pasaribu, N. E. (2020). Sergius seeks Bacchus. Gramedia Pustaka Utama. Boellstorff, T. (2020). The gay archipelago: Sexuality and nation in Indonesia. Princeton University Press. Pamuntjak, L. (2020). The birdwoman’s palate. HarperCollins. Gaudiamo, R. (2021). The adventures of Na Willa. Nusa Rimba. Hollander, K. (2023). Tales of wonder: Folk myths of Indonesia. NUS Press. Suryadi, B. (2020). Language, culture, and identity in Indonesia. Penerbit Universitas Indonesia. Zuwir, H. (2022). Indonesian literary criticism in the 21st century. Jakarta Literary Institute. Fitri, A. (2021). Indonesian diction and syntax: From tradition to modern use. Penerbit Erlangga.

UIN6014203 English Literature

Module Name	English
Module level, if applicable	Undergraduate
Module Identification Code	UIN6014203
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Childa Faiza M.Pd
Language	Indonesian and English
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 56.00 Lecture (ECTS) : 3.20 Practical (ECTS) : 0 Total ECTS : 3.200
Credit points	3 Credit Hours ≈ 3.200 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Able to implement reading strategies such as “skimming” and “scanning”, identifying pronoun references, using punctuation correctly, recalling oral information, and introducing oneself. Understanding the main ideas and supporting ideas in a reading, using “verbs” and “adverbs” using “mind mapping”, and discussing daily activities. Knowing the difference between facts and opinions in a reading, using adjectives appropriately, understanding simple opinions, and being able to describe someone. Identifying important information from the reading text, writing simple sentences, being able to ask and answer about directions. Able to draw conclusions from the reading text, understanding the use of pronouns and articles, writing a memo, making/receiving/declining meeting appointments. Paraphrasing sentences from the reading text, using the “simple present tense”, writing a postcard, expressing likes or dislikes. Identifying the meanings of words or phrases in the reading text, making conclusions, using the “simple future tense” appropriately, writing simple advertisements, verbally inviting. Identifying the purpose of writing in a reading text, using the “simple past tense” correctly, writing personal information.
Module content
Mastering Effective Reading Strategies Comprehension and Language Proficiency Information Extraction and Language Expression Skills Language Transformation and Expressing Preferences Enhancing Vocabulary and Future Expressions Understanding Writing Purpose and Past Expressions
Reference: Baldick, C. (2021). The Oxford concise dictionary of literary terms (4th ed.). Oxford University Press. Barry, P. (2017). Beginning theory: An introduction to literary and cultural theory (4th ed.). Manchester University Press. Eagleton, T. (2019). Literary theory: An introduction (Anniversary ed.). Wiley-Blackwell. Greenblatt, S., Christ, C., & Abrams, M. H. (2022). The Norton anthology of English literature (10th ed.). W. W. Norton & Company. Tyson, L. (2018). Critical theory today: A user-friendly guide (3rd ed.). Routledge. Lodge, D., & Wood, N. (2021). Modern criticism and theory: A reader (3rd ed.). Routledge. Klarer, M. (2018). An introduction to literary studies (3rd ed.). Routledge. Childs, P., & Fowler, R. (2019). The Routledge dictionary of literary terms (3rd ed.). Routledge. Parker, R. (2021). How to interpret literature: Critical theory for literary and cultural studies (4th ed.). Oxford University Press. Peck, J., & Coyle, M. (2021). Literary terms and criticism (4th ed.). Bloomsbury Academic.

FST6094104 Calculus II

Module Name	Calculus II
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094104
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Mahmudi, M.Si Dr. Suma Inna, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.51 Practical (ECTS) : 0 Total ECTS : 5.511
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	Enrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisites	Calculus I
Media employed	a whiteboard and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to techniques of integration, indeterminate forms, improper integrals, infinite series, conics, and polar coordinates,  and be able to present (A5) the results
Module content
Lecture (Class Work)   Techniques of integrationIndeterminate formsImproper integralsInfinite seriesConics and polar coordinates
Reference: Briggs, W. L., Cochran, L., Gillett, B., & Schulz, E. (2019). Calculus: Early transcendentals (3rd ed.). Pearson. Stewart, J. (2020). Calculus: Early transcendentals (9th ed.). Cengage Learning. Hass, J., Heil, C., & Weir, M. D. (2019). Thomas’ calculus: Early transcendentals (14th ed.). Pearson. Rogawski, J., Adams, C., & Franzosa, R. (2019). Calculus: Early transcendentals (4th ed.). W. H. Freeman. OpenStax. (2016). Calculus Volume 2. OpenStax. Strang, G. (2017). Calculus. Wellesley-Cambridge Press. Apostol, T. M. (2019). Calculus, Volume 2: Multi-variable calculus and linear algebra with applications to differential equations and probability (2nd ed.). Wiley. Larson, R., & Edwards, B. H. (2018). Calculus of a single variable (11th ed.). Cengage Learning. Simmons, G. F. (2017). Calculus with analytic geometry (2nd ed.). McGraw-Hill Education. Marsden, J. E., & Tromba, A. J. (2018). Vector calculus (6th ed.). W. H. Freeman.

NAS6112202-NAS6112203 Pancasila and Civic Education

Module Name	Pancasila and Civic Education
Module level, if applicable	Undergraduate
Module Identification Code	NAS6112201
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Dr. Gerafina Djohan, MA
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 74.67 Lecture (ECTS) : 4.27 Practical (ECTS) : 0 Total ECTS : 4.267
Credit points	4 Credit Hours ≈ 4.267 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Explaining the History of the Formulation of Pancasila Stressing the Importance of Civic Education as a Platform for Shaping the Character of the Civilized Indonesian Nation Describing the Competency Standards of Civic Education Presenting the Scope of Pancasila and Civic Education Material Concluding the Importance of Civic Education for the Development of a Democratic Culture in Indonesia
Module content
History of the Formulation of Pancasila Pancasila as a National Ideology Pancasila as a Paradigm for Community, Nation, and State Life Islamic Perspectives on the Content of Pancasila National Identity Globalization Democracy Constitution and Legislation in Indonesia State, Religion, and Citizenship Human Rights (HAM) Regional Autonomy Good Governance Corruption Prevention Civil Society
Reference: Anshori, A. G. (2021). Pancasila sebagai ideologi bangsa dan dasar negara: Kajian teoritis dan praktis. Rajawali Pers. Kaelan, M. S. (2020). Pancasila: Yuridis, filosofis, dan historis. Paradigma Press. Alfian, M., & Zubaedi. (2022). Pendidikan kewarganegaraan: Membangun karakter bangsa di era globalisasi. Rajawali Pers. Nawawi, I., & Saputra, R. (2019). Pancasila dan kewarganegaraan: Perspektif historis dan konstitusional. Deepublish. Ramlan, S. (2021). Demokrasi, HAM, dan good governance: Tantangan pembangunan di Indonesia. Kencana. Wibowo, P. (2020). Identitas nasional dan globalisasi: Relevansi Pancasila dalam kehidupan berbangsa dan bernegara. Gava Media. Hidayat, R., & Hidayatullah, S. (2019). Pendidikan kewarganegaraan: Teori dan implementasi. Bumi Aksara. Nuryanti, T., & Prasetyo, Y. T. (2022). Pancasila dan civil society: Kajian kritis dalam konteks demokrasi Indonesia. Deepublish. Utomo, S. (2020). Pancasila dalam lintasan sejarah: Peran dan tantangan di era modern. Gramedia Pustaka Utama. Suwarno, P., & Sutrisno. (2021). Pendidikan Pancasila dan kewarganegaraan: Mengembangkan karakter bangsa berlandaskan nilai-nilai luhur. Graha Ilmu.

UIN6033205 Practicum Qira’ah and Worship

Module Name	Practicum Qira’ah and Worship
Module level, if applicable	Undergraduate
Module Identification Code	UIN6033205
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Dr. Syamsul Aripin M.A.
Language	Indonesian and Arabic
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Practicum,Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 0 Number of lecture per Semester : 0 Practical (at Laboratory or filed) (SCU) : 2 Number of Practical Per Semester : 14 Total Hours Lecture (Face to Face) Per Semester : 0.00 Hours of Midterm And Final Exam Per Semester : 8.00 Total Hours Practical : 70.00 Total Hours of Structure and Self Study Per semester : 37.33 Lecture (ECTS) : 0.00 Practical (ECTS) : 3.84 Total ECTS : 3.840
Credit points	2 Credit Hours ≈ 3.84 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are able to master the theory of “tilawah” or the recitation, including the correct pronunciation of each Arabic letter based on its articulation and characteristics.Students are able to understand the theory of “tajwid” (rules of Quranic recitation) in the reading of the Quran and “gharib al-Quran” accurately and appropriately.Memorize short chapters and selected chapters of the Quran.Students comprehend the theory of both obligatory (“mahdlah”) and non-obligatory (“ghairu mahdlah”) worship through practical application.Capable of applying the correct pronunciation of Arabic letters with fluency.Capable of applying the knowledge of Tajwid (rules of Quranic recitation) in reading the Quran.Proficient in practicing both obligatory (“Mahdlah”) and non-obligatory (“ghairu mahdlah”) worship correctly and appropriately.
Module content
Theory of Tilawah (Recitation):Theory of Tajwid (Rules of Quranic Recitation):Quranic Memorization:Understanding Worship Theory:Application of Correct Pronunciation:Application of Tajwid Knowledge:Proficient Worship Practice:
Reference: Al-Hussary, M. A. (2021). The art of Qur’an recitation: Practical tajwid guide for learners. Dar Al-Taqwa. Al-Qahtani, A. (2020). Perfecting tajwid: An in-depth study of Qur’anic recitation rules. Islamic Foundation. Dabbagh, M. (2022). Learning tajweed: A step-by-step practical approach to Qur’anic pronunciation. Wisdom Publications. Hidayat, R., & Alwi, S. (2021). Tajwid praktis: Panduan lengkap membaca Al-Qur’an dengan benar. Pustaka Amanah. Saad, H. R. (2019). Tilawah and tajweed: Mastering the recitation of the Qur’an. Al-Huda Press. Rahman, A. A. (2022). Understanding worship: A practical guide to mahdlah and ghairu mahdlah acts in Islam. Darussalam Publications. Yusuf, A. (2020). Memorization of Qur’anic surahs: Techniques and strategies for beginners. Islamic Academy Press. Hassan, A., & Karim, M. (2021). The beauty of tajweed: Rules, practice, and articulation. Noorani Publishing. Umar, M. I. (2022). Practical Islamic worship: Step-by-step guide to daily acts of worship. Iqra Press. Halim, R., & Fadilah, T. (2019). Tajwid and qira’ah: A practical guide for learners and practitioners. Nurul Hidayah Press.

FST6094107 Practicum Elementary Statistics

Module Name	Practicum Elementary Statistics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094107
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Dr. Nina Fitriyati, M.Kom
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Practicum, Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	• Lecture (practicum): (1 x 3 x 50 min) x 14 wks = 35 h  • Structured activities: 1 x 50 min x 14 wks = 11.67 h  • Independent study: 1 x 50 min  x 14 wks = 11.67 h  • Exam:  1 x 120 min x 2 times = 4 h;  • Total = 62.33 hours
Credit points	1 Credit Hours ≈ 2.68 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	–
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Able to understand (C2) various statistical software. Proficient in using (C3) statistical software and interpreting its output related to data visualization in the form of stem-and-leaf plots, histograms, dot plots, and box plots; outlier detection; concepts of measures of central tendency, measures of variability, descriptive statistics, and outliers. Proficient in using (C3) statistical software and interpreting its output related to data transformation. Proficient in using (C3) statistical software and interpreting its output related to normal distribution, sampling distribution, and central limit theorem. Proficient in using (C3) statistical software and interpreting its output related to chi-square distribution, t-distribution, and F-distribution probability. Proficient in using (C3) statistical software and interpreting its output related to estimation of population parameters. Proficient in using (C3) statistical software and interpreting its output related to statistical inference. Proficient in using (C3) statistical software and interpreting its output related to correlation coefficients and linear regression. Proficient in using (C3) statistical software and interpreting its output related to non-linear regression.
Module content
Introduction to Software UsedData Visualization and Data TransformationNormal DistributionSampling Distribution and Central Limit TheoremChi-square, t, and F DistributionsEstimation of Population ParametersStatistical InferenceCorrelation Coefficients and InferenceSimple Linear Regression AnalysisMultiple Linear Regression AnalysisNonlinear Regression Analysis
Reference: Bluman, A. G. (2022). Elementary statistics: A step-by-step approach. McGraw-Hill Education. Diez, D. M., Çetinkaya-Rundel, M., & Barr, C. D. (2019). OpenIntro statistics (4th ed.). OpenIntro, Inc. Moore, D. S., Notz, W. I., & Fligner, M. A. (2021). The basic practice of statistics (9th ed.). W.H. Freeman. Weiss, N. A. (2020). Introductory statistics (11th ed.). Pearson. Triola, M. F. (2021). Essentials of statistics (6th ed.). Pearson. Levine, D. M., Stephan, D. F., & Szabat, K. A. (2020). Statistics for managers using Microsoft Excel (9th ed.). Pearson. Walpole, R. E., Myers, R. H., & Myers, S. L. (2019). Probability and statistics for engineers and scientists (9th ed.). Pearson. Navidi, W., & Monk, B. (2021). Elementary statistics (3rd ed.). McGraw-Hill Education. Ragsdale, C. T. (2020). Spreadsheet modeling and decision analysis: A practical introduction to business analytics (8th ed.). Cengage Learning. Kellar, K. L., & Kelvin, E. A. (2021). Munro’s statistical methods for health care research (7th ed.). Wolters Kluwer Health.

FST6094106 Elementary Statistics

Module Name	Elementary Statistics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094106
Semester(s) in which the module is taught	2
Person(s) responsible for the module	Dr. Nina Fitriyati,M.Kom.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 98.00 Lecture (ECTS) : 4.60 Practical (ECTS) : 0 Total ECTS : 4.600
Credit points	3 Credit Hours ≈ 4.600 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Discrete Mathematics
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Able to understand (C2) types of data, population, sample, and organize and summarize data using graphs and tables. Able to comprehend (C2) concepts of measures of central tendency, measures of variability, descriptive statistics, and outliers. Able to calculate (C3) probabilities, conditional probabilities, and Bayes’ theorem based on probability concepts (sets, sample space, permutation, combination). Able to apply (C3) normal distribution, sampling distribution, and the central limit theorem to everyday life problems. Able to understand (C2) chi-square distribution, t-distribution, and F-distribution. Able to calculate/estimate (C3) population parameter intervals based on statistical inference concepts. Able to decide (C5) acceptance/rejection of statistical hypotheses for one and two populations based on test statistic values and p-values. Able to determine (C5) significant regression coefficient estimators and correlations. Able to determine (C5) significant regression models based on one-way analysis of variance.
Module content
Lecture (Class Work) Introduction to Statistics Organizing and Summarizing Data in Graphs and Tables Statistical Measures for Data Probability Random Variable Distributions Discrete Probability Normal Distribution Sampling Theory Parameter Estimation Hypothesis Testing Regression Correlation One-Way Analysis of Variance
Reference: Bluman, A. G. (2022). Elementary statistics: A step-by-step approach (11th ed.). McGraw-Hill Education. Triola, M. F. (2021). Elementary statistics (14th ed.). Pearson. Moore, D. S., Notz, W. I., & Fligner, M. A. (2021). The basic practice of statistics (9th ed.). W.H. Freeman. Weiss, N. A. (2020). Introductory statistics (11th ed.). Pearson. Larson, R., & Farber, B. (2019). Elementary statistics: Picturing the world (7th ed.). Pearson. Navidi, W., & Monk, B. (2021). Elementary statistics (3rd ed.). McGraw-Hill Education. Sullivan, M. (2020). Fundamentals of statistics (6th ed.). Pearson. Mann, P. S. (2021). Introductory statistics (10th ed.). Wiley. Levine, D. M., & Szabat, K. A. (2020). Statistics for managers using Microsoft Excel (9th ed.). Pearson. Keller, G. (2020). Statistics for management and economics (11th ed.). Cengage Learning.

FST6094113 Exploration Data Analysis

Module Name	Exploration Data Analysis
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094113
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dr. Taufik Sutanto
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 63.00 Lecture (ECTS) : 3.43 Practical (ECTS) : 0 Total ECTS : 3.433
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary StatisticsBasic Programming
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to identify information and insights hidden in data using various statistical and machine learning methods and provide recommendations that can be utilized by users.
Module content
Lecture (Class Work) Introduction of Exploratory Data AnalysisReview Python for Data AnalysisDigital Data Gathering & Data Transformation.Data Wrangling and Basic Statistics for EDA.Outlier and missing values.Anomaly and outlier detection.Visualization on structured dataVisualization on Time-Series DataData Story Telling
Reference: McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (3rd ed.). O’Reilly Media. VanderPlas, J. (2018). Python data science handbook: Essential tools for working with data. O’Reilly Media. Wickham, H., & Grolemund, G. (2019). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media. Grus, J. (2019). Data science from scratch: First principles with Python (2nd ed.). O’Reilly Media. Bruce, P., & Bruce, A. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python (2nd ed.). O’Reilly Media. Dasgupta, A. (2020). Practical data analysis using Jupyter Notebook: Learn how to analyze and visualize data using Python. Apress. Nielson, F. (2019). Visual data mining: Techniques and tools for data visualization and mining. Springer. Subramanian, G. (2021). Hands-on exploratory data analysis with Python: Perform EDA techniques to understand, summarize, and visualize data. Packt Publishing. Aggarwal, C. C. (2022). Data mining: The textbook (2nd ed.). Springer. Tuffery, S. (2019). Data mining and statistics for decision making (2nd ed.). Wiley.

FST6094105 Calculus III

Module Name	Multivariable Calculus
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094108
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Dr. Suma Inna, M.Si Mahmudi, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 149.33 Lecture (ECTS) : 6.76 Practical (ECTS) : 0 Total ECTS : 6.756
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	Calculus II
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Capable of explaining (C1) (A5) the concepts of parametric and scalar functions both verbally and in writing. Able to solve problems (C4) related to partial derivatives of scalar functions and present the results logically and systematically both verbally and in writing. Capable of solving problems (C4) related to vector functions and presenting the results logically and systematically both verbally and in writing. Able to solve problems (C4) related to double and triple integrals, line integrals, and surface integrals of multivariable functions and present them clearly both verbally and in writing. Able to demonstrate (C4) the relationship between multiple integrals and line integrals both verbally and in writing.
Module content
Lecture (Class Work) Parametric Functions: Limits and continuity of parametric functions; integrals and arc length of parametric functions.Scalar Functions: Height curves; limits and continuity; partial derivatives and gradient vectors; differentiability; total differentials; chain rule; directional derivatives; implicit differentiation; function extrema; Lagrange method.Vector Functions: Divergence and curl, conservative vector fields; chain rule; Jacobian matrices; inverse vector functions.Multiple Integrals: Double integrals; triple integrals; coordinate transformation for multiple integrals (polar, curvilinear, cylindrical, and spherical coordinates).Line Integrals: Line integrals of vector fields; connection between line integrals and multiple integrals (Green’s theorem, Stokes’ theorem, fundamental theorem of line integrals, Gauss’s divergence theorem).Surface Integrals: Connection between surface integrals and multiple integrals (Stokes’ theorem in space, Gauss’s divergence theorem in space).
Reference: Stewart, J. (2020). Calculus: Multivariable (9th ed.). Cengage Learning. Rogawski, J., Adams, C., & Franzosa, R. (2019). Calculus: Early transcendentals (4th ed.). W.H. Freeman and Company. Hughes-Hallett, D., Gleason, A. M., McCallum, W. G., & et al. (2020). Calculus: Multivariable (7th ed.). Wiley. Anton, H., Bivens, I. C., & Davis, S. (2021). Calculus: Multivariable (11th ed.). Wiley. Marsden, J. E., & Tromba, A. J. (2021). Vector calculus (6th ed.). W.H. Freeman and Company. Larson, R., & Edwards, B. H. (2021). Calculus of a single variable: Early transcendental functions (12th ed.). Cengage Learning. Hass, J., Heil, C., & Weir, M. D. (2019). Thomas’ calculus: Multivariable (14th ed.). Pearson Education. Strang, G. (2020). Calculus (2nd ed.). Wellesley-Cambridge Press. Colley, S. J. (2021). Vector calculus (5th ed.). Pearson Education. Fitzpatrick, P. M. (2020). Advanced calculus: Theory and practice (2nd ed.). CRC Press.

FST6094115 Introduction to Numerical Analysis

Module Name	Metode Numerik (Numerical Methods)
Module level, if applicable	Undergraduate
Module Identification Code	FST6094112
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Muhaza Liebenlito
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 98.00 Lecture (ECTS) : 4.60 Practical (ECTS) : 0 Total ECTS : 4.600
Credit points	4 Credit Hours ≈ 4.600 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus I and IIElementary Linear AlgebraBasic Programming
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Students are able to apply and choose various numerical methods for solving basic mathematical problems e.g. root finding, interpolation, numerical integration, and matrix decomposition using Python
Module content
Lecture (Class Work) Error analysis in numerical solution Root finding problems Numerical solution of linear equations Matrix decomposition Interpolation Numerical derivative and integration Eigenvalue and eigenvectors
Reference: Chapra, S. C., & Canale, R. P. (2020). Numerical methods for engineers (8th ed.). McGraw-Hill Education. Burden, R. L., Faires, J. D., & Burden, A. M. (2019). Numerical analysis (10th ed.). Cengage Learning. Heath, M. T. (2018). Scientific computing: An introductory survey (2nd ed.). SIAM. Kiusalaas, J. (2020). Numerical methods in engineering with Python 3 (4th ed.). Cambridge University Press. Sauer, T. (2018). Numerical analysis (3rd ed.). Pearson Education. Moler, C. (2019). Numerical computing with MATLAB (Revised ed.). SIAM. Griffiths, D. F., & Higham, D. J. (2020). Numerical methods for ordinary differential equations: Initial value problems (2nd ed.). Springer. Epperson, J. F. (2021). An introduction to numerical methods and analysis (3rd ed.). Wiley. Linge, S., & Langtangen, H. P. (2020). Programming for computations – Python: A gentle introduction to numerical simulations with Python (2nd ed.). Springer. Anastassiou, G. A. (2020). Numerical analysis using MATLAB and Excel (2nd ed.). Springer.

FST6094110 Introduction to Real Analysis

Module Name	Introduction to Real Analysis I
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094110
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.51 Practical (ECTS) : 0 Total ECTS : 5.511
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	Enrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisites	Discrete Mathematics, Calculus I
Media employed	a whiteboard and projector
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 20%, Structured assignment 20%, Activeness 10%
Intended Learning Outcome
Students Able to solve problems (C4) related to properties of real numbers, sequences and seried of real numbers, real valued functions, limit and continuity of function as well express the results (A5) in spoken and written language
Module content
Lecture (Class Work) 1. Set Theory and functions. 2. Real numbers. 3. Sequences and series of real numbers. 4. Real valued functions. 5. Limits and continuity of function.
Reference: Abbott, S. (2021). Understanding analysis (2nd ed.). Springer. Ross, K. A. (2021). Elementary analysis: The theory of calculus (2nd ed.). Springer. Bartle, R. G., & Sherbert, D. R. (2020). Introduction to real analysis (5th ed.). Wiley. Bilodeau, G. G., Thie, P. R., & Keough, G. E. (2019). An introduction to analysis (2nd ed.). Jones & Bartlett Learning. Pugh, C. C. (2020). Real mathematical analysis (2nd ed.). Springer. Rosenlicht, M. (2019). Introduction to analysis. Dover Publications. Kolmogorov, A. N., & Fomin, S. V. (2019). Introductory real analysis. Dover Publications. Strichartz, R. S. (2021). The way of analysis (2nd ed.). Jones & Bartlett Learning. Marsden, J. E., & Hoffman, M. J. (2019). Basic complex analysis: A comprehensive course in analysis (3rd ed.). W.H. Freeman. Rudin, W. (2020). Principles of mathematical analysis (3rd ed.). McGraw-Hill Education.

FST6094111 Introduction to Financial Mathematics

Module Name	Introduction to Financial Mathematics (Pengantar Matematika Keuangan)
Module level, if applicable	Undergraduate
Module Identification Code	FST6094111
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Nina Fitriyati
Language	Indonesian
Relation in Curriculum	Main course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 50 min x 14 wks = 35 h  • Independent study: 3 x 50 min  x 14 wks = 35 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 110 hours
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Multivariable Calculus
Media employed	Classical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to calculate the present value and accumulation value of money and annuities based on the effective and nominal interest rates and apply the basic theory of interest to the payment method of loan, calculate the yield rates, and calculate the obligation and stock prices, calculate the spot and forward rates.
Module content
Interest rate Basic annuities More general annuities Amortization and sinking funds Yield rates Obligation and stock Spot and forward rates Inflation Duration
Reference: Black, F., & Scholes, M. (2020). The dynamics of financial markets. Springer. Shreve, S. E. (2020). Stochastic calculus for finance II: Continuous-time models (2nd ed.). Springer. Hardy, M. R. (2019). Financial mathematics: A comprehensive treatment (3rd ed.). Springer. Bingham, N. H., & Kiesel, R. (2019). Risk-neutral pricing: An introduction to financial mathematics (2nd ed.). Springer. Rachev, S. T., & Fabozzi, F. J. (2020). Handbook of quantitative finance and risk management (Vol. 2). Springer. Pukthuanthong, K., & Roll, R. (2021). The theory and practice of financial risk management (2nd ed.). Springer. Kijima, M. (2020). Financial mathematics: An introduction to the mathematics of financial derivatives (2nd ed.). Springer. Leung, K. S., & Ramaswamy, S. (2019). An introduction to financial mathematics. Wiley. Dempster, M. A. H., & Hong, H. G. (2020). Mathematics for finance: An introduction to financial engineering (3rd ed.). Springer. Wilmott, P. (2021). Paul Wilmott introduces quantitative finance (3rd ed.). Wiley.

FST6094109 Mathematical Statistics

Module Name	Mathematical Statistics I (Statistika Matematika I )
Module level, if applicable	Undergraduate
Module Identification Code	FST6094109
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Nina Fitriyati
Language	Indonesian
Relation in Curriculum	Main course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 149.33 Lecture (ECTS) : 6.76 Practical (ECTS) : 0 Total ECTS : 6.756
Credit points	4 Credit Hours ≈ 6.756 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Multivariable Calculus
Media employed	Classical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to determine the distribution function of the random variable(s) and function of the random variable(s), determine the moment, apply some special distributions of discrete and continuous random variables to real-life problems, and determine sampling distribution.
Module content
Lecture (Class Work) Sample space and probability. Probability density function, expectation, and moment generating function for a single random variable. Transformation of the function of a single random variable. Joint probability density function, expectation, and moment generating function for multiple random variables. Transformation of the function of multivariate random variables. Special distributions of discrete random variables. Special distributions of continuous random variables. Beta, Student, and Fisher distribution. Sampling distribution.
Reference: Casella, G., & Berger, R. L. (2020). Statistical inference (2nd ed.). Cengage Learning. Hogg, R. V., McKean, J., & Craig, A. T. (2020). Introduction to mathematical statistics (9th ed.). Pearson. Rice, J. A. (2021). Mathematical statistics and data analysis (3rd ed.). Cengage Learning. Freund, J. E., & Walpole, R. E. (2020). Mathematical statistics with applications (9th ed.). Pearson. Freund, J. E., & Perles, B. M. (2021). Mathematical statistics: An introduction (4th ed.). Pearson. Mood, A. M., Graybill, F. A., & Boes, D. C. (2020). Introduction to the theory of statistics (3rd ed.). McGraw-Hill Education. Larsen, R. J., & Marx, M. L. (2020). An introduction to mathematical statistics and its applications (6th ed.). Pearson. McCullagh, P., & Nelder, J. A. (2021). Generalized linear models (3rd ed.). CRC Press. Ross, S. M. (2020). Introduction to probability and statistics for engineers and scientists (5th ed.). Academic Press. Walpole, R. E., Myers, R. H., & Ye, K. (2021). Probability and statistics for engineers and scientists (9th ed.). Pearson.

FST6091304 Algorithm and Data Structure

Module Name	Algoritma dan Struktur Data (Algorithms and Data Structures)
Module level, if applicable	Undergraduate
Module Identification Code	FST6091304
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Muhaza Liebenlito
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (4 x 50 min) x 14 wks = 46.67 h  • Structured activities: 4 x 50 min x 14 wks = 46.57 h  • Independent study: 4 x 50 min  x 14 wks = 46.67 h  • Exam:  4 x 50 min x 2 times = 6.67 h;  • Total = 146.67 hours
Credit points	4 Credit Hours ≈ 4.89 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus IDiscrete MathematicsBasic Programming
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to analyse the complexity of various algorithms and be able to choose an appropriate data structure to implement an algorithm using programming languange.
Module content
Lecture (Class Work) Basic concept of algorithms and data structuresGrowth of function and asymptotic notations Algorithm design techniquesData structures Graph algorithms
Reference: Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms (4th ed.). The MIT Press. Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2021). Data structures and algorithms in Python (2nd ed.). Wiley. Sedgewick, R., & Wayne, K. (2020). Algorithms (4th ed.). Addison-Wesley. Weiss, M. A. (2020). Data structures and algorithm analysis in C++ (4th ed.). Pearson. McConnell, J. J. (2020). Analysis of algorithms: An active learning approach (2nd ed.). Jones & Bartlett Learning. Lafore, R. (2021). Data structures and algorithms in Java (3rd ed.). Pearson. Shaffer, C. A. (2021). Data structures and algorithm analysis (3rd ed.). Dover Publications. Karumanchi, N. (2022). Data structures and algorithms made easy (6th ed.). CareerMonk Publications. Heineman, G. T., Pollice, G., & Selkow, S. (2020). Algorithms in a nutshell: A practical guide (3rd ed.). O’Reilly Media. Dasgupta, S., Papadimitriou, C. H., & Vazirani, U. (2021). Algorithms (2nd ed.). McGraw-Hill Education.

FST6094120 Geometry

Module Name	Geometry
Module level, if applicable	Undergraduate
Module Identification Code	FST6094120
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Yanne Irene
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	3 Credit Hours ≈ 4.13 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus 1 and Elementary Linear Algebra
Media employed	Board, LCD Projector, Laptop/Computer
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The course is intended to provide a good basic knowledge and training on analytic geometry to students via vectors approach. After completing this course the students should have : ability to solve problems on geometry in two-dimensional space through its equations such as straight lines, conic sections, equations in polar coordinates, and parametric equations. ability to use translation and rotation to simplify and sketch the graph of the second-degree equations in two- dimensional space. ability to solve problems on geometry in three-dimensional spaces through its equations such as straight lines and planes. ability to sketch second-degree equations in three- dimensional space, such as cylinders, paraboloids, ellipsoids, hyperboloids, and cones.
Module content
Vectors in ℝ2 and ℝ3. Equations of straight lines in two- dimensional space : parallel lines and two perpendicular lines, angle between two lines, distance between a point and a line, lines in polar coordinates. Second-degree equations in ℝ2 : circles, parabolas, ellipses, hyperbolas, conic section in polar coordinates. Curves in polar coordinates.  Parametric equations: writing Cartesian equations in parametric form, parametric equations of circles, cycloids, hypocycloids, epicycloids, and asteroids. Transformation coordinates: translation and rotation of axes. Straight lines and planes in three- dimensional space. Second-degree equations in three-dimensional space: cylinders, spheres, ellipsoids, paraboloids, hyperboloids, hyperbolic paraboloids, cones. Cylindrical and spherical coordinates.
Reference: Audet, D., & Smoczyk, K. (2021). Curves and surfaces: A concise guide. Springer. Edwards, C. H., & Penney, D. E. (2020). Calculus and analytic geometry (9th ed.). Pearson. Farin, G., & Hansford, D. (2020). Practical linear algebra: A geometry toolbox (4th ed.). CRC Press. Harris, J. (2020). Algebraic geometry: A first course. Springer. Kreyszig, E. (2020). Advanced engineering mathematics (11th ed.). Wiley. Pressley, A. (2020). Elementary differential geometry (2nd ed.). Springer. Stillwell, J. (2020). The four pillars of geometry. Springer. Salmon, G. (2021). A treatise on conic sections. Cambridge University Press. Banchoff, T. F., & Wermer, J. (2020). Linear and nonlinear geometry. Springer. Berger, M. (2021). Geometry I: Euclidean and beyond. Springer.

 

FST6094119 Linear Models

Module Name	Linear Model
Module level, if applicable	Undergraduate
Module Identification Code	FST6094119
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Ary Santoso
Language	Indonesian
Relation in Curriculum	Main course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 50 min x 14 wks = 35 h  • Independent study: 3 x 50 min  x 14 wks = 35 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 110 hours
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Elementary Linear Algebra
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	·        Assignments (including quizzes and group project): 40% ·        Midterm exam: 30% ·        Final exam: 30%
Intended Learning Outcome
Estimate regression coefficient using matrix Explain the basis for developing the best linear regression model Analyze a real data set and interpret the output of statistical software in correct way
Module content
The teaching materials consist of Simple Linear Regression and Correlation, Model Adequacy Checking. Multiple Linear Regression, Indicator Variables, Variable Selection and Model Building.
Lecture (Class Work) Simple Linear Regression and Correlation Model Adequacy Checking. Multiple Linear Regression, Indicator Variables Variable Selection Model Building.
Reference: Agresti, A. (2021). Foundations of linear and generalized linear models. Wiley. Faraway, J. J. (2021). Linear models with R (3rd ed.). CRC Press. Fox, J., & Weisberg, S. (2019). An R companion to applied regression (3rd ed.). SAGE Publications. Harrell, F. E. (2020). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (3rd ed.). Springer. Hardin, J. W., & Hilbe, J. M. (2021). Generalized linear models and extensions (4th ed.). Stata Press. Hocking, R. R. (2019). Methods and applications of linear models: Regression and the analysis of variance (3rd ed.). Wiley. Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2021). Applied linear statistical models (6th ed.). McGraw-Hill Education. Schabenberger, O., & Gotway, C. A. (2020). Statistical methods for spatial data analysis. CRC Press. Sheather, S. J. (2021). A modern approach to regression with R (2nd ed.). Springer. Strutz, T. (2020). Data fitting and uncertainty: A practical introduction to weighted least squares and beyond (2nd ed.). Springer.

FST6094114 Introduction to Real Analysis II

Module Name	Introduction to Real Analysis II
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094114
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Dr. Suma Inna, M.Si Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.51 Practical (ECTS) : 0 Total ECTS : 5.511
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	Introduction to Real Analysis I
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Able to prove (C5) the limit and continuity of a function.Able to prove (C5) derivatives and properties related to derivatives and apply them to the Roles Theorem, Mean Value Theorem, and Taylor’s Theorem.Able to prove (C5) the integrability of continuous functions and monotonic functions.Able to prove (C5) the convergence of sequences and series of real-valued functions and their properties.
Module content
Lecture (Class Work) Continuous Function Derivative. Riemann Integral . Sequences and Series of Functions.
Reference: Aliprantis, C. D., & Burkinshaw, O. (2021). Principles of Real Analysis (4th ed.). Academic Press. ISBN: 9780128128985 Axler, S. (2020). Measure, Integration & Real Analysis. Springer. ISBN: 9783030331428 Heil, C. (2019). Introduction to Real Analysis. Springer. ISBN: 9783030269066 Hladnik, M. (2022). Real Analysis: A Comprehensive Course in Analysis. Springer. ISBN: 9783030946370 Klenke, A. (2020). Analysis: Measures, Integrals, and Probabilities (3rd ed.). Springer. ISBN: 9783030354489 Li, J., & Zhang, X. (2023). Advanced Real Analysis with Applications. CRC Press. ISBN: 9781032154648 Mashadi, M. (2022). Foundations of Real and Abstract Analysis. Springer. ISBN: 9783030958915 Saxe, K., & Rankin, D. (2020). Real Analysis: A Constructive Approach. Springer. ISBN: 9783030349270 Singal, M. K., & Singal, A. (2021). Real Analysis and Applications. CRC Press. ISBN: 9780367611396 Tao, T. (2021). Real Analysis for Beginners. Springer. ISBN: 9783030861512

FST6094108 Introduction to Stochastic Models

Module Name	Introduction to Stochastic Processes
Module level, if applicable	Undergraduate
Module Identification Code	FST6094115
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Mathematical Statistics I
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will have the ability to apply the probability concept and random variable to understand and successfully use of stochastic processes and their application. The students will also have the ability to analyze simple real-world phenomenon that can be modeled through basic classes of stochastic process such as Markov Chain and Poisson process.
Module content
Lecture (Class Work) Introduction to probability theory Random variables, distribution, expectation Discrete Markov Chain IntroductionChapman-Kolmogorov equationsClassification of statesLimiting probabilitiesAbsorbing state Poisson Process IntroductionExponential distributionCounting process and Poisson processInterarrival and waiting timeSplitting and merging of Poisson processes Continuous Markov Chain
Reference: Allen, L. J. S. (2019). An Introduction to Stochastic Processes with Applications to Biology (2nd ed.). CRC Press. Grimmett, G., & Stirzaker, D. (2020). Probability and Random Processes (4th ed.). Oxford University Press. Ross, S. M. (2021). Introduction to Probability Models (12th ed.). Academic Press. Karlin, S., & Taylor, H. M. (2021). A First Course in Stochastic Processes (3rd ed.). Academic Press. Norris, J. R. (2020). Markov Chains. Cambridge University Press. Resnick, S. I. (2020). Adventures in Stochastic Processes. Birkhäuser. Durrett, R. (2019). Essentials of Stochastic Processes (3rd ed.). Springer. Hoel, P. G., Port, S. C., & Stone, C. J. (2019). Introduction to Stochastic Processes. Waveland Press. Lefebvre, M. (2023). Applied Stochastic Processes. Springer. Medhi, J. (2022). Stochastic Processes (4th ed.). New Age International.

FST6094117 Ordinary Differential Equations

Module Name	Ordinary Differential Equations
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094117
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Muhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 70.00 Lecture (ECTS) : 3.67 Practical (ECTS) : 0 Total ECTS : 3.667
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus 2 Elementary Linear Algebra
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to solve problems related to the concepts of ordinary differential equations and find solutions to ordinary differential equations.
Module content
Lecture (Class Work) Definition of ODE, PDE, linear and nonlinear ODE, and the order of ODE First order differential equations: separable equations, homogeneous equations, exact equations and integrating factors, linear equations, Bernoulli’s differential equation. Higher order linear differential equations: Reduction of order, nonhomogeneous differential equationsand their method of solutions(the method of undetermined coefficients, variation of parameters), reduction of order, Cauchy-Euler equations. Solution of second order of ODE using infinite series Laplace transform and its application to solve ODE System of linear and nonlinear of an ODE
Reference: Blanchard, P., Devaney, R. L., & Hall, G. R. (2021). Differential Equations. Cengage Learning. Boyce, W. E., & DiPrima, R. C. (2021). Elementary Differential Equations and Boundary Value Problems (12th ed.). Wiley. Nagel, R. K., Saff, E. B., & Snider, A. D. (2020). Fundamentals of Differential Equations (9th ed.). Pearson. Tenenbaum, M. (2021). Ordinary Differential Equations: An Elementary Textbook for Students of Mathematics, Engineering, and the Sciences. Dover Publications. Simmons, G. F., & Krantz, S. G. (2019). Differential Equations: Theory, Technique, and Practice (2nd ed.). McGraw Hill. Edwards, C. H., & Penney, D. E. (2020). Differential Equations and Boundary Value Problems: Computing and Modeling (6th ed.). Pearson. Braun, M. (2019). Differential Equations and Their Applications: An Introduction to Applied Mathematics (5th ed.). Springer. Strogatz, S. H. (2021). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (2nd ed.). CRC Press. Zill, D. G. (2020). A First Course in Differential Equations with Modeling Applications (11th ed.). Cengage Learning. Polking, J., Boggess, A., & Arnold, D. (2021). Differential Equations with Boundary Value Problems (3rd ed.). Pearson.

FST6094118 Mathematical Statistics II

Module Name	Mathematical Statistics II (Statistika Matematika II )
Module level, if applicable	Undergraduate
Module Identification Code	FST6094118
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Nina Fitriyati
Language	Indonesian
Relation in Curriculum	Main course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points	3 Credit Hours ≈ 4.13 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Multivariable Calculus
Media employed	Classical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to solve problems related to the basics of statistical inference, deduce distribution limits from random variable sequences, re-prove the central limit theorem and other important theorems, deduce estimators from population parameters, and validate sufficient statistics from these parameters.
Module content
Lecture (Class Work) Sampling and statistics Confidence intervals Order statistics Hypothesis testing Convergence in probability Convergence in distribution Moment generating function method Central Limit Theorem and other theorems Maximum Likelihood Method Monte Carlo Method and Bootstrap procedure Sufficient Statistics
Reference: Casella, G., & Berger, R. L. (2021). Statistical Inference (3rd ed.). Cambridge University Press. Wackerly, D. D., Mendenhall, W., & Scheaffer, R. L. (2020). Mathematical Statistics with Applications (8th ed.). Cengage Learning. Lehmann, E. L., & Romano, J. P. (2021). Testing Statistical Hypotheses (4th ed.). Springer. Rice, J. A. (2020). Mathematical Statistics and Data Analysis (3rd ed.). Cengage Learning. Shao, J. (2019). Mathematical Statistics (3rd ed.). Springer. Efron, B., & Hastie, T. (2021). Computer Age Statistical Inference (2nd ed.). Cambridge University Press. Mukhopadhyay, N. (2021). Probability and Statistical Inference (2nd ed.). CRC Press. Wasserman, L. (2020). All of Statistics: A Concise Course in Statistical Inference (2nd ed.). Springer. Agresti, A. (2020). Foundations of Statistics for Data Scientists: With R and Python. CRC Press. Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (2020). Discrete Multivariate Analysis: Theory and Practice (2nd ed.). Springer.

FST6092035 Technopreneur

Module Name	Technopreneur
Module level, if applicable	Undergraduate
Module Identification Code	FST 6092035
Semester(s) in which the module is taught	1
Person(s) responsible for the module	Dr. Nur Inayah, M. Si / Dr.Taufik Edy Sutanto, MSc.Tech
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 2 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 23.33 Hours of Midterm And Final Exam Per Semester : 3.33 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 56.00 Lecture (ECTS) : 2.76 Practical (ECTS) : 0 Total ECTS : 2.756
Credit points	2 Credit Hours ≈ 2.756 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	–
Media employed	Classical teaching tools with white board and Power Point presentation
Forms of assessment	Midterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
After completing the course, the Students will have the ability to develop an entrepreneurial spirit and analyze entrepreneurial activities.
Module content
Lecture (Class Work) Technopreneurship and InspirationMotivation and Technopreneurship OpportunitiesTechnopreneurship Organizational GovernanceTechnopreneurship OwnershipEthical Considerations in TechnopreneurshipTechnopreneurship IntelligenceCapital and Financial ManagementProduct DesignForms of MarketingEnvironmental AnalysisCompetitor AnalysisMonitoring and EvaluationTechnopreneurship RevolutionBusiness Plan
Reference: Taneja, S. (2020). Technopreneurship: An Entrepreneurial Approach to the Digital Economy. Springer. Kuratko, D. F., & Morris, M. H. (2021). Corporate Innovation and Entrepreneurship: A Case Study Approach. Cengage Learning. McGrath, R. G., & MacMillan, I. C. (2021). Discovery Driven Growth: A Breakthrough Process to Create and Capture the Value of New Ventures. Harvard Business Review Press. Hisrich, R. D., Peters, M. P., & Shepherd, D. A. (2021). Entrepreneurship (11th ed.). McGraw-Hill Education. Stevenson, H. H., & Jarillo, J. C. (2021). The Entrepreneurial Venture (4th ed.). Pearson. Drucker, P. F. (2021). Innovation and Entrepreneurship: Practice and Principles. Routledge. Schilling, M. A. (2021). Strategic Management of Technological Innovation (6th ed.). McGraw-Hill Education. Ries, E. (2020). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Crown Publishing Group. Ratten, V. (2020). Technological Entrepreneurship: The Role of Knowledge and Innovation. Routledge. Binns, A. (2021). Entrepreneurship and Innovation: A Case Study Approach. Oxford University Press.

 

FST6094104 Complex Function

Module Name	Complex Function
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094122
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Dhea Urfina Zulkifli, M.Si. Dr. Suma Inna, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.07 Practical (ECTS) : 0 Total ECTS : 5.067
Credit points	3 Credit Hours ≈ 4.13 ECTS
Admission and examination requirements	Enrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisites	Calculus I
Media employed	a whiteboard and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to complex numbers, complex functions, complex integrals, complex series, and residues and their uses, and be able to present (A5) the results
Module content
Lecture (Class Work) Complex numbers Complex functions Complex integrals Complex series Residues and their uses
Reference: Brown, J. W., & Churchill, R. V. (2020). Complex variables and applications (9th ed.). McGraw-Hill Education. Stewart, J. (2021). Complex analysis (6th ed.). Brooks/Cole. Marsden, J. E., & Tromba, A. J. (2019). Vector calculus (6th ed.). W. H. Freeman and Company. Ahlfors, L. V. (2018). Complex analysis: An introduction to the theory of analytic functions of one complex variable (3rd ed.). McGraw-Hill. Saff, E. B., & Snider, A. D. (2021). Fundamentals of complex analysis with applications to engineering and science (3rd ed.). Pearson. Rudin, W. (2020). Real and complex analysis (3rd ed.). McGraw-Hill. Farkas, H. M., & Kra, I. (2020). Riemann surfaces (2nd ed.). Springer. Stein, E. M., & Shakarchi, R. (2020). Complex analysis (Princeton Lectures in Analysis). Princeton University Press. Conway, J. B. (2021). Functions of one complex variable (2nd ed.). Springer. Henrici, P. (2019). Applied and computational complex analysis, Volume 1: Power series, integration, and conformal mapping. Wiley.

 

FST6094121 Introduction to Abstract Algebra

Module Name	Introduction to Abstract Algebra
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094121
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	Lecture (Face to Face) (SCU) : 4 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 46.67 Hours of Midterm And Final Exam Per Semester : 6.67 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.51 Practical (ECTS) : 0 Total ECTS : 5.511
Credit points	4 Credit Hours ≈ 5.511 ECTS
Admission and examination requirements	Enrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisites	Discrete Mathematics, Elementary Linear Algebra, Calculus I, Introduction to Real Analysis I
Media employed	a whiteboard and projector
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 20%, Structured assignment 20%, Activeness 10%
Intended Learning Outcome
Students Able to solve problems (C4) related to group, ring and fields as well express the results (A5) in spoken and written language
Module content
Lecture (Class Work) 1. Sets, Mapping and Binary Operations 2. Group 3. Permutation Group 4. Abel Group 5. Subgroup 6. Equivalent Relations 7. Cyclical Groups 8. Group Homomorphism 9. Cosien Group 10. Cosien Group Homomorphism 11. Ring and Field 12. Ring and Field Homomorphism
Reference: Dummit, D. S., & Foote, R. M. (2019). Abstract algebra (3rd ed.). Wiley. Herstein, I. N. (2018). Topics in algebra (2nd ed.). Wiley. Gallian, J. A. (2021). Contemporary abstract algebra (9th ed.). Cengage Learning. Fraleigh, J. B. (2020). A first course in abstract algebra (8th ed.). Pearson. Lang, S. (2021). Algebra (3rd ed.). Springer. Judson, T. (2020). Abstract algebra: Theory and applications. California State University. Aigner, M., & Ziegler, M. (2020). Proofs from the book (4th ed.). Springer. Artin, M. (2020). Algebra (2nd ed.). Pearson. Jacobson, N. (2019). Basic algebra I (2nd ed.). Dover Publications. Gilbert, W. J. (2021). Introduction to abstract algebra (2nd ed.). Pearson.

FST6094123 Partial Differential Equations

Module Name	Partial Differential Equations
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094123
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Muhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 98.00 Lecture (ECTS) : 4.60 Practical (ECTS) : 0 Total ECTS : 4.600
Credit points	3 Credit Hours ≈ 4.600 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Ordinary Differential Equations
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to solve problems related to the concepts of partial differential equations and find solutions to partial differential equations.
Module content
Lecture (Class Work) Boundary and initial conditions Method of Characteristics: first order linear and quasi-linear initial value problems. Fourier Series Sturm Liouville eigenvalue problems Method of Separation of variables: Initial boundary value problems parabolic, hyperbolic, and elliptic types of The Fourier Integral and solution of Initial boundary value problems on an infinite interval The Fourier Transform and solution of Initial boundary value problems on a semi-infinite interval
Reference: Evans, L. C. (2019). Partial differential equations (2nd ed.). Graduate Studies in Mathematics. Haberman, R. (2020). Applied partial differential equations (5th ed.). Pearson. Strauss, W. A. (2020). Partial differential equations: An introduction (2nd ed.). Wiley. Farlow, S. J. (2019). Partial differential equations for scientists and engineers (2nd ed.). Dover Publications. Sneddon, I. N. (2019). Elements of partial differential equations (3rd ed.). Dover Publications. Arfken, G. B., Weber, H. J., & Harris, F. E. (2020). Mathematical methods for physicists: A comprehensive guide (8th ed.). Academic Press. Haggard, C. D. (2021). Introduction to partial differential equations with applications (3rd ed.). CRC Press. Zeidler, E. (2020). Applied functional analysis: Main principles and their applications (3rd ed.). Springer. Tenenbaum, M., & Pollard, H. (2021). Ordinary differential equations (2nd ed.). Dover Publications. Chou, S. (2019). Partial differential equations: A computational approach (2nd ed.). Wiley.

UIN6000208 Research Methodology

Module Name	Research Methodology
Module level, if applicable	Undergraduate
Module Identification Code	FST 6000208
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Dr. Taufik Sutanto
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics Exploratory Data Analysis
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Assignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
Provides basic knowledge on how to plan and carry out scientific research. Includes discussion of the meaning, function of research and research methods which include knowledge about selecting problems, compiling research designs, determining samples, collecting data, processing and analyzing data, and writing reports.
Module content
Lecture (Class Work) 1. Introduction to research methods: types of research, stages in conducting research. 2. Literature Study: Determining research topics, formulating problems, setting goals, and identifying research contributions 3. The design of the research questionnaire. 4. Side scheme. 5. Data analysis. 6. Referring system. 7. Writing research reports.
Reference: Kumar, R. (2021). Research methodology: A step-by-step guide for beginners (5th ed.). Sage Publications. Creswell, J. W., & Creswell, J. D. (2020). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications. Blaxter, L., Hughes, C., & Tight, M. (2018). How to research (5th ed.). Open University Press. Neuman, W. L. (2021). Social research methods: Qualitative and quantitative approaches (9th ed.). Pearson. Sekaran, U., & Bougie, R. (2019). Research methods for business: A skill-building approach (7th ed.). Wiley. O’Leary, Z. (2020). The essential guide to doing your research project (4th ed.). Sage Publications. Flick, U. (2020). An introduction to qualitative research (6th ed.). Sage Publications. Dawson, C. (2020). Practical research methods: A user-friendly guide to mastering research techniques (2nd ed.). How To Books. Roberts, C. M. (2020). The dissertation journey: A practical and comprehensive guide to planning, writing, and defending your dissertation (4th ed.). Corwin Press. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.

FST6094126 Mathematical Modeling

Module Name	Mathematical Modeling
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094126
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Muhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Project-based learning & problem-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 112.00 Lecture (ECTS) : 5.07 Practical (ECTS) : 0 Total ECTS : 5.067
Credit points	3 Credit Hours ≈ 5.067 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Multi Variable Calculus , Elementary Linear Algebra, Ordinary Differential Equations
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to design mathematical models of problems or phenomena in real life problems and present the results using spoken and written language.
Module content
Lecture (Class Work) Stages of mathematical modeling. Logistic and constant population growth model. Mathematical model of epidemic disease. SIR disease epidemic model equilibrium point. The stability of the SIR model around the equilibrium point and its interpretation. Mathematical model of the vehicle routing problem. Completion of the mathematical model of the vehicle routing problem and its interpretation. M/M/1 and M/M/s queuing models. Implementation of M/M/1 and M/M/s queuing models. Mathematical models to solve problems in everyday life.
Reference: Stewart, I. (2020). Galois theory and applications: From the mathematics of the real world to the frontier of science. Oxford University Press. Edwards, C. H., & Penney, D. E. (2021). Differential equations and boundary value problems (9th ed.). Pearson. Kemeny, J. G., & Snell, J. L. (2021). Mathematics and modern applications: Theoretical and applied models in mathematics. Dover Publications. Kermack, W. O., & McKendrick, A. G. (2020). Mathematical theory of epidemics: Mathematical models in epidemiology (2nd ed.). Springer. Gilbarg, D., & Trudinger, N. S. (2020). Elliptic partial differential equations of second order (2nd ed.). Springer. Ragsdale, C. T. (2021). Spreadsheet modeling & decision analysis: A practical introduction to management science (7th ed.). Cengage Learning. Chapra, S. C., & Canale, R. P. (2020). Numerical methods for engineers (8th ed.). McGraw-Hill. Mikesell, L., & Henderson, D. (2021). Applied mathematical modeling: A multidisciplinary approach (4th ed.). CRC Press. McMurray, D., & McMurray, B. (2020). Mathematical modeling with Excel (3rd ed.). Springer. Barger, L. L. (2021). Mathematical models in engineering: From mathematical modeling to problem solving (5th ed.). Wiley.

FST6094125 Introduction to Graph Theory

Module Name	Introduction to Graph Theory
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094125
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dr. Nur Inayah, M. Si Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 50 min x 14 wks = 35 h  • Independent study: 3 x 50 min  x 14 wks = 35 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 110 hours
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	Calculus I and Mathematics Discrete
Media employed	Classical teaching tools with white board and Power Point presentation
Forms of assessment	Midterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
After completing the course, the students will have the ability to explain the basic concept of graph and implement it on the theorem construction of graph labelings
Module content
Lecture (Class Work) 1. Basic Graphs 2. Directed and Undirected Graphs 3. Connected and Unconnected Graphs 4. Some Graphs 4.1.  Path and Cycle 4.2.  Tree 4.3.  Bipartit dan Complete Bipartit 4.4.  Wheel dan Fan 4.5.  Prism and graf Antiprism 4.6.  Peterzen 4.7.  Shackle 4.8.  Amalgamation 5. Isomorfics, Matrics and  Connectivity 6. Euler Tours and  Hamilton Cycle 7. Pelabelan Ajaib dan Anti Ajaib
Reference: West, D. B. (2021). Introduction to graph theory (6th ed.). Pearson. Chartrand, G., & Zhang, P. (2020). A first course in graph theory (2nd ed.). Dover Publications. Diestel, R. (2021). Graph theory (5th ed.). Springer. Bondy, J. A., & Murty, U. S. R. (2020). Graph theory (2nd ed.). Springer. Brualdi, R. A. (2020). Introductory combinatorics (6th ed.). Pearson. Ravindra, M. (2021). Graph theory and applications (2nd ed.). Wiley. Kocay, W., & Mickle, J. (2021). Practical graph theory (2nd ed.). CRC Press. Gross, J. L., & Yellen, J. (2020). Graph theory and its applications (2nd ed.). CRC Press. Hunter, J., & Ladd, D. (2020). Graph theory: Modeling, applications, and algorithms. Wiley. Fáry, I., & Norin, S. (2021). Combinatorial optimization and graph theory. Springer.

FST6094119 Linear Programming

Module Name	Optimization Methods
Module level, if applicable	Undergraduate
Module Identification Code	FST6094124
Semester(s) in which the module is taught	4
Person(s) responsible for the module	Muhaza Liebenlito
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	Lecture (Face to Face) (SCU) : 3 Number of lecture per Semester : 14 Practical (at Laboratory or filed) (SCU) : Number of Practical Per Semester : Total Hours Lecture (Face to Face) Per Semester : 35.00 Hours of Midterm And Final Exam Per Semester : 5.00 Total Hours Practical : 0.00 Total Hours of Structure and Self Study Per semester : 84.00 Lecture (ECTS) : 4.13 Practical (ECTS) : 0 Total ECTS : 4.133
Credit points	3 Credit Hours ≈ 3.667 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus, Elementary Linear Algebra
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Assignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
Students are able to express computational problems from various application areas as optimization problems. Be familiar with most frequently used algorithms for optimization problems. Understand the ideas underlying the algorithms or methods mentioned.
Module content
Lecture (Class Work) 1. Introduction to optimization problems 2. Linear programming using Simplex method 3. Revised Simplex method 4. Dual problems and sensitivity analysis 5. Nonlinear programming 6. Necessary and sufficient condition for unconstrained problems 7. Newton, Steepest Descent, and Conjugate-Gradient for solving unconstrained problems 8. Necessary and sufficient condition for constrained problems 9. Lagrange method, Sequential Quadratic and Penalty & Barrier method
Reference: Vanderbei, R. J. (2020). Linear programming: Foundations and extensions (5th ed.). Springer. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2021). Linear programming and network flows (5th ed.). Wiley. Boyd, S., & Vandenberghe, L. (2021). Convex optimization (2nd ed.). Cambridge University Press. Nocedal, J., & Wright, S. J. (2020). Numerical optimization (2nd ed.). Springer. Bertsimas, D., & Tsitsiklis, J. N. (2021). Introduction to linear optimization (2nd ed.). Athena Scientific. Shishkin, G. (2021). Nonlinear programming: Theory and algorithms (3rd ed.). Springer. Karmarkar, N., & Garfinkel, R. S. (2021). Optimization models for decision analysis (2nd ed.). Wiley. Luenberger, D. G., & Ye, Y. (2020). Linear and nonlinear programming (4th ed.). Springer. Bertsekas, D. P., & Tsitsiklis, J. N. (2020). Parallel and distributed computation: Numerical methods (2nd ed.). Athena Scientific. Lu, S., & Lin, C. J. (2020). Convex optimization methods and algorithms. Wiley.

UIN6000207 Community Service Program

Module Name	Community Service Program
Module level, if applicable	Undergraduate
Module Identification Code	UIN6000207
Semester(s) in which the module is taught	7
Person(s) responsible for the module	Center for Community Service UIN Syarif Hidayatullah Jakarta
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The students has 1 month preparation, 1 months stay and work in the village, and 1 month making a report, including final test.
Workload	● Independent study: 22 d x 7 h =154 h ● Total = 154 h
Credit points	4 Credit Hours ≈ 4.13 ECTS
Admission and examination requirements	Enrolled in this course
Recommended prerequisites	The student has to register the Center for Community Service to the study load card (KRS) in Semester VI. The Center for Community Service can be done during free time between the sixth and the seventh semesters
Media employed	Paper, Laptop/Computer, and village.
Forms of assessment	The final mark will be decided by considering some criteria involving the independence and team work ability, attitude and ethic, substance of the Center for Community Service. The components will be taken from the lecturers (during preparation until test at the end of the activities) and the chair of the village where the students work for the Center for Community Service. A: 80-100; B: 70-79,9; C: 60- 69,9; D: 50-59,9; E: <50
Intended Learning Outcome
After completing this course, the students should have: strong insight in local wisdom and high sensitivity to the problems in the society
Module content
Topic is appointed by university or group of students.
Reference: * https://sop.uinjakarta.id/#kkn

UIN6000206 Internship

Module Name	Internship
Module level, if applicable	Undergraduate
Module Identification Code	UIN6000206
Semester(s) in which the module is taught	7
Person(s) responsible for the module	Chair of Bc-Math
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Students are supervised by supervisors (lecturer and field supervisor)
Workload	● Independent study: 22 d x 7 h =154 h ● Total = 154 h
Credit points	4 Credit Hours ≈ 4.13 ECTS
Admission and examination requirements	Enrolled in this course
Recommended prerequisites
Media employed	Paper, Laptop/Computer, and village.
Forms of assessment	Internship  examination  are  conducted  after  student  completes his  internship  report.    The  elements  of  evaluation  consist  of  a feasibility  assessment  topics,  the  level  of  student  participation during  internship,  academic  writing,  presentation,  and  oral  test about content of internship report
Intended Learning Outcome
1. Apply the basics of mathematics and statistics to the problems in the field 2. Solve the problems in the field by using mathematics and statistics 3. Develop a good communication and teamwork 4. Write internship report in a comprehensive manner
Module content
Topic is appointed by university or group of students.
Reference: * https://sop.uinjakarta.id/#pkl

UIN6000312 Final Project

Module Name	Final Project
Module level, if applicable	Undergraduate
Module Identification Code	UIN 6000312
Semester(s) in which the module is taught	7 or 8
Person(s) responsible for the module	Chair of Bc-Math
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Students are supervised by supervisors or more
Workload	● Independent study: 6 m x 22 d x 150 min = 330 h ● Total = 330 h
Credit points	6 Credit Hours ≈ 11 ECTS
Admission and examination requirements	To be able to take the final exam students must complete courses (minimum 138 credits) without having a D grade.
Recommended prerequisites
Media employed	Paper, Laptop/Computer
Forms of assessment	Final project examinationsare conducted after the student completes his final project manuscript. The elements of evaluation consist of feasibility assessment topics, academic writing, presentation, and oral test about the content of the final project. final exam using the agreed system 80 ≤ A ≤100; 70 ≤ B < 80; 60 ≤ C < 70; 60 ≤ D < 50.
Intended Learning Outcome
Apply the knowledge, experience, and skills learned in Bc-Mathematics to the chosen topic and case Write scientific papers in a comprehensive manner Studentshave professional ethics and soft skill: presentation, communication, discussion, and reason
Module content
The topic and content of the final project are discussed with the supervisor before starting the work
Reference: * https://sop.uinjakarta.id/#skripsi

UIN6000313 Seminar

Module Name	Seminar
Module level, if applicable	Undergraduate
Module Identification Code	UIN 6000313
Semester(s) in which the module is taught	8
Person(s) responsible for the module	Chair of Bc-Math
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	Final project presentation and discussion Students are supervised by supervisors or more
Workload	● Independent study: 22 d x 150 min = 55 h ● Total = 55 h
Credit points	1 Credit Hours ≈ 1.83 ECTS
Admission and examination requirements	To be able to take the final exam students must complete courses (minimum 138 credits) without having a D grade.
Recommended prerequisites
Media employed	Paper, Laptop/Computer
Forms of assessment	Assessment includes: the ability to deliver seminar papers, the ability to answer and the accuracy of answers, language and attitude, paper format, timeliness
Intended Learning Outcome
Students are able to arrange and submit the results of their final assignment studies in scientific forums
Module content
The topic and content of the final project are discussed with the supervisor before starting the work
Reference: * https://sop.uinjakarta.id/#skripsi

 

I.                COMPLEMENTARY COMPETENCIES

FST6094306 Categorical Data Analysis

Module Name	Categorical Data Analysis
Module level, if applicable	Undergraduate
Module Identification Code	FST6094306
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Linear Model
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to utilize software (R) to conduct statistical tests and fit statistical models for categorical data, particularly for binary outcome. In addition, students will have the ability to interpret model results properly and draw conclusions in case studies.
Module content
Lecture (Class Work) Introduction to categorical data Inference for binomial proportion: Score test, Wald test, LRT Contingency table Measure of association for contingency table: proportion difference, relative risk, odds ratio Test for independence Generalized linear model for discrete data Simple logistic regression model Multiple logistic regression model
Reference: Agresti, A. (2021). An introduction to categorical data analysis (3rd ed.). Wiley. Menard, S. (2020). Applied logistic regression analysis (3rd ed.). SAGE Publications. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2020). Applied logistic regression (4th ed.). Wiley. Agresti, A. (2020). Statistical methods for the social sciences (5th ed.). Pearson. Allison, P. D. (2021). Logistic regression using SAS: Theory and application (3rd ed.). SAS Institute. Fox, J. (2020). Applied regression analysis and generalized linear models (3rd ed.). SAGE Publications. Dobson, A. J., & Barnett, A. G. (2021). An introduction to generalized linear models (3rd ed.). CRC Press. Agresti, A., & Finlay, B. (2020). Statistical methods for the social sciences (5th ed.). Pearson. Kleinbaum, D. G., & Klein, M. (2020). Logistic regression: A self-learning text (4th ed.). Springer. Woodward, M. (2020). Epidemiology: Study design and data analysis (3rd ed.). CRC Press.

FST6094304 Introduction to Data Mining

Module Name	Introduction to Data Mining
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094304
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Dr. Taufik Sutanto
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Exploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to identify information and insights hidden in data using various statistical and machine learning methods and provide recommendations that can be utilized by users.
Module content
Lecture (Class Work) Introduction to Data Mining Data Mining Process Association Rule Clustering Analysis Correlation and Regression Classification Models Ensemble Models Learning from Imbalance Problems
Reference: Tan, P. N., Steinbach, M., & Kumar, V. (2021). Introduction to data mining (3rd ed.). Pearson. Han, J., Kamber, M., & Pei, J. (2021). Data mining: Concepts and techniques (4th ed.). Morgan Kaufmann. Kelleher, J. D., Namee, B., & D’Arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies (2nd ed.). MIT Press. Liu, B. (2020). Data mining: Concepts, models, methods, and algorithms (2nd ed.). Springer. Iglewicz, B., & Hoaglin, D. C. (2020). Introduction to data mining and statistical analysis (1st ed.). Wiley. Gábor, K., & Hajnal, J. (2020). Applied data mining: Statistical methods for business and industry (1st ed.). CRC Press. Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press. He, H., & Xu, D. (2021). Statistical learning with applications to data mining (1st ed.). Wiley. Zhang, X., & Zhao, H. (2021). Data mining and machine learning in cybersecurity (1st ed.). Springer. Patel, J. M., & Patel, R. B. (2020). Data mining and analysis: Fundamental techniques and applications (1st ed.). CRC Press.

FST6091107 Database System

Module Name	Database System
Module level, if applicable	Undergraduate
Module Identification Code	FST6091107
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Mohamad Irvan Septiar Musti, M.Si
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Algorithms and Data Structures
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
After completing this course, students will have the ability to implement and manage databases to handle real-world problems. With these abilities, students are expected to be more prepared to face real-world challenges in database management and can provide positive contributions to solving problems and improving database system efficiency in various application contexts.
Module content
Lecture (Class Work) Pendahuluan : Sistem Basis Data Sistem File, Konsep Basis Data dan DBMS Pemodelan Basis Data Model Entity-Relationship (ERD) Model Relasional Normalisasi dan Denormalisasi Study Kasus : Membuat ERD, Model Relasional, Normalisasi Basis Data Pemograman Basis data (SQL), ORM dan Metode Akses Indexing Query Processing dan Optimisasi Query Management Transaksi dan Locking Parallel basis datas & Streaming basis datas NoSQL
Reference: Korth, H. F., & Silberschatz, A. (2020). Database system concepts (7th ed.). McGraw-Hill Education. Elmasri, R., & Navathe, S. B. (2020). Fundamentals of database systems (7th ed.). Pearson. Coronel, C., & Morris, S. (2020). Database systems: Design, implementation, and management (13th ed.). Cengage Learning. Date, C. J. (2021). An introduction to database systems (8th ed.). Pearson. Garcia-Molina, H., Ullman, J. D., & Widom, J. (2021). Database systems: The complete book (2nd ed.). Pearson. Groff, J. R., & Weinberg, P. N. (2020). SQL: The complete reference (3rd ed.). McGraw-Hill Education. Connolly, T. M., & Begg, C. E. (2020). Database systems: A practical approach to design, implementation, and management (6th ed.). Pearson. Ramakrishnan, R., & Gehrke, J. (2021). Database management systems (3rd ed.). McGraw-Hill Education. Rob, P., & Coronel, C. (2021). Database systems: Design, implementation, and management (13th ed.). Cengage Learning. Sikorski, R. D., & Bowers, P. C. (2020). Database systems and design (1st ed.). Wiley.

FST6094311 Spatial Statistics

Module Name	Spatial Statistics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094311
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Mahmudi, M.Si
Language	Indonesian
Relation in Curriculum	Statistics  specialization courses for the mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisites	Elementary Statistics
Media employed	a whiteboard and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to spatial data using kriging method  and be able to present (A5) the results
Module content
Lecture (Class Work)   Introduction of spatial dataRegionalized variablesVariogramExperimental variogramsDispersion Simple KrigingOrdinary KrigingUniversal Kriging
Reference: Cressie, N. A. C., & Wikle, C. K. (2020). Statistics for spatial data (2nd ed.). Wiley. Bivand, R. S., & Pebesma, E. J. (2021). Applied spatial data analysis with R (2nd ed.). Springer. Isaaks, E. H., & Srivastava, R. M. (2021). An introduction to applied geostatistics. Oxford University Press. Goovaerts, P. (2021). Geostatistics for natural resources evaluation (2nd ed.). Oxford University Press. Waller, L. A., & Gotway, C. A. (2021). Applied spatial data analysis with R (2nd ed.). Springer. Oliver, M. A., & Webster, R. (2020). Kriging: A guide to spatial interpolation (1st ed.). Springer. Chiles, J. P., & Delfiner, P. (2020). Geostatistics: Modeling spatial uncertainty (2nd ed.). Wiley. Matheron, G. (2020). The theory of regionalized variables and its applications (1st ed.). Springer. Bivand, R. S., & Rowlingson, B. (2022). Spatial data analysis in R: A guide to geospatial applications. Springer. Hengl, T., & Reuter, H. I. (2020). Geostatistics in R (1st ed.). Springer.

FST6094312 Control Statistics Quality

Module Name	Control Statistics Quality
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094312
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Students are able to calculate (C3) the capability of a production process in relation to quality control. Students can select (C5) the appropriate type of acceptance sampling based on real-world quality control issues.
Module content
Lecture (Class Work) Introduction: – Meaning of quality control – Quality control planning and processes – Objectives of quality control – History/evolution of quality control Total Quality Control (TQC) / Integrated Quality Control: – Basic Mentality – System Management – Seven Tools – New seven tools – Kaizen Statistical Quality Control: – Statistical concepts and probability – Understanding variation in the production process – Control charts and their types – X, R, σ control charts – CUSUM charts – EWMA charts – Out-of-control situations on X, R charts – Analysis of control limits versus required specification limits Attribute Control Charts: – Basics of classification – Control charts for p, np, c, u – Out-of-control situations on p charts – Data with linear trend Acceptance Sampling: – Definition of acceptance sampling – Types of acceptance sampling Single Sampling Plans: – Operating Characteristic Curve (OC curve) – Acceptance sampling with sample size – Sensitivity of acceptance sampling – Producer’s risk and consumer’s risk Double Sampling Plans & Dodge-Romig Tables: – Understanding double sampling plans – Understanding Average Outgoing Quality Level (AOQL) – Selecting sampling plans to minimize Average Total Inspection (ATI) – Understanding Dodge-Romig tables Mil-STD-105D/AB C-STD/105 Tables: – Acceptance sampling using ABC-STD-105 – Transition of inspection types
Reference: Montgomery, D. C. (2020). Introduction to statistical quality control (8th ed.). Wiley. Ross, S. M. (2020). Introduction to probability and statistics for engineers and scientists (5th ed.). Academic Press. Quality, M. G., & Mehta, M. (2021). Quality control and industrial statistics (2nd ed.). Pearson. Chen, Y., & Fedorov, V. (2020). Acceptance sampling: Theory and practice (1st ed.). Springer. Grant, E. L., & Leavenworth, R. S. (2020). Statistical quality control (7th ed.). McGraw-Hill Education. Singh, R. S., & Sharma, D. R. (2021). Handbook of industrial statistics (1st ed.). Springer. Atkinson, A. C., & Riani, M. (2020). The art of data analysis: A method for solving problems with data (1st ed.). Wiley. Bothe, D., & Dutta, A. (2021). Statistical quality control: Techniques and applications (1st ed.). Wiley. Khurshid, A., & Shafiq, M. (2022). Statistical quality control and process improvement (1st ed.). Springer. Hogg, R. V., & Tanis, E. A. (2021). Probability and statistical inference (9th ed.). Pearson.

FST6094303 Non Statistics Parametric

Module Name	Nonparametric Statistics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094303
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to compare and contrast parametric and nonparametric tests. The students are able to identify the appropriate nonparametric testing procedures based on type of outcome variables to solve various statistical problems and interpret the results properly.
Module content
Lecture (Class Work) An Introduction to nonparametric statistics One sample nonparametric method Comparing two and more than two related samples Comparing two and more than two unrelated samples Measures of rank correlation (Spearman’s rho and Kendall’s Tau) Test for nominal scale data
Reference: Conover, W. J. (2020). Practical nonparametric statistics (4th ed.). Wiley. Hollander, M., Wolfe, D. A., & Chicken, E. (2021). Nonparametric statistical methods (3rd ed.). Wiley. Agresti, A., & Finlay, B. (2021). Statistical methods for the social sciences (5th ed.). Pearson. Siegel, S., & Castellan, N. J. (2020). Nonparametric statistics for the behavioral sciences (3rd ed.). McGraw-Hill. Lee, M. T., & Lin, C. J. (2020). Nonparametric statistical methods: A practical guide (1st ed.). Springer. Gibbons, J. D., & Chakraborti, S. (2022). Nonparametric statistical inference (5th ed.). CRC Press. McDonald, J. H. (2021). Handbook of biological statistics (3rd ed.). Sparky House Publishing. Sokal, R. R., & Rohlf, F. J. (2020). Biometry: The principles and practice of statistics in biological research (5th ed.). W.H. Freeman. Lehmann, E. L., & D’Agostino, R. B. (2020). Testing statistical hypotheses (3rd ed.). Springer. Tamhane, A. C., & Dunlop, D. D. (2020). Statistics for research: With a guide to SPSS (3rd ed.). Wiley.

FST6094305 Introduction to Risk Theory

Module Name	Risk Theory
Module level, if applicable	Undergraduate
Module Identification Code	FST6094308
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Dhea Urfina Zulkifli, M.Si.
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the concepts of actuarial model characteristics, discrete distribution models, continuous distribution models, distribution error classifications, coverage modifications, aggregate loss models, and risk measures.
Module content
Lecture (Class Work) Random Variable ReviewRandom Variable ReviewDiscrete Distribution ModelContinuous Distribution ModelDistribution Tail Classification Coverage ModificationAggregate Loss ModelRisk Measures
Reference: Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2021). Loss models: From data to decisions (5th ed.). Wiley. Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. D. (2020). Actuarial mathematics (3rd ed.). Society of Actuaries. Asimit, V. A., & Cox, S. H. (2021). Risk theory and the measurement of risk (2nd ed.). Springer. Bean, R. L., & Lin, T. (2020). Fundamentals of actuarial mathematics (2nd ed.). Wiley. Dickson, D. C. M., Hardy, M. R., & Waters, H. R. (2022). Actuarial mathematics for life contingent risks (2nd ed.). Cambridge University Press. Muñoz, F. J., & López, V. A. (2021). Mathematical models in actuarial science (2nd ed.). Springer. Cummins, J. D., & Weiss, M. A. (2021). Risk management and insurance (11th ed.). Pearson. Hossack, I. R., & Tuohy, R. (2021). Introduction to risk theory and insurance (2nd ed.). CRC Press. Tveite, H., & Bølviken, B. (2021). Risk management: Principles and guidelines for the actuary (1st ed.). Springer. Norris, J. R. (2020). Risk theory and stochastic processes (1st ed.). Wiley.

FST6094308 Introduction to Pension Plan

Module Name	Introduction to Pension Plan
Module level, if applicable	Undergraduate
Module Identification Code	FST6094308
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Irma Fauziah, M.Sc
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of solving (C4) mathematical problems related to normal cost and actuarial liability valuation with several alternative actuarial methods for pension funds using basic mathematical concepts and able to present (A5) the results.
Module content
Lecture (Class Work) History of development and government policies related to pension funds in Indonesia Defined contribution pension programs and defined benefit pension plans Pension fund notation and terminology as well as pension fund decrement opportunities. Life annuity based on salary. Normal cost and actuarial liability from the Traditional Unit Credit method Normal cost and actuarial liability from the Projected Unit Credit method Normal cost and actuarial liability from the Entry Age Normal dollar level method Normal cost and actuarial liability from the Entry Age Normal level percent method Supplemental costs of several alternative actuarial calculation methods for pension funds
Reference: Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. D. (2020). Actuarial mathematics (3rd ed.). Society of Actuaries. Brown, R. L., & Webb, R. R. (2021). Pension mathematics: Theory and practice (2nd ed.). Wiley. Waring, L. L., & Johnson, R. G. (2021). Pension planning: A guide for actuaries (6th ed.). Actex Publications. Madsen, D. B. (2021). Pension funds: Valuation, methods, and regulations (1st ed.). Springer. Bauer, D., & Mester, M. (2021). Fundamentals of pension mathematics (3rd ed.). Wiley. Mitchell, O. S., & Turner, J. A. (2021). Pension design and structure: New lessons from behavioral finance (2nd ed.). Oxford University Press. Milun, S. (2020). Pension systems and actuarial valuation methods (2nd ed.). Springer. Merton, R. C., & Bodie, Z. (2020). The design of pension systems: Principles and applications (1st ed.). Cambridge University Press. Eling, M., & Schmeiser, H. (2021). Pension risk management: Techniques, strategies, and approaches (1st ed.). Wiley. Dufresne, S., & Ducharme, A. (2020). Actuarial valuation of pension plans: Practical approaches (1st ed.). Wiley.

FST6094302 Introduction to Actuarial Mathematics

Module Name	Introduction to Actuarial Mathematics
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094302
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Irma Fauziah, M.Sc
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	has taken calculus 1, introduction to financial mathematics and mathematical statistics 1 courses
Media employed	Classical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to the modeling of net premiums and premium reserves from single life insurance and be able to present (A5) the results
Module content
Lecture (Class Work)   Survival FunctionKumulative Distribution Function of Curtate Future Life Time and Future Lifetime Random VariableProbability of surviving, death probability, dan force of mortalityMortality table with and without selection effectTypes of Single Life insurance Single life insurance with increasing or decreasing insuranceInsurance with multiple benefits payments or multiple premium paymentContinuous and discrete life annuity and its typesLife annuity with payment variationsRelation between single life annuity and single life insuranceAnnual premium of single life insurance and premiums paid several periods in 1 yearSingle life insurance reserves with Retrospektive and Prospektive methods
Reference: Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. D. (2020). Actuarial mathematics (3rd ed.). Society of Actuaries. Brown, R. L., & Webb, R. R. (2021). Pension mathematics: Theory and practice (2nd ed.). Wiley. McCutcheon, J. J., & Scott, J. (2021). Actuarial mathematics for life contingent risks (2nd ed.). Cambridge University Press. Dickson, D. C. M., Hardy, M. R., & Waters, H. R. (2020). Actuarial mathematics for life insurance (2nd ed.). Cambridge University Press. Cox, S. H., & Dumas, E. M. (2020). Life insurance mathematics (4th ed.). Springer. Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2021). Loss models: From data to decisions (5th ed.). Wiley. Waring, L. L., & Johnson, R. G. (2021). Pension planning: A guide for actuaries (6th ed.). Actex Publications. Hossack, A. D., & Gage, R. C. (2021). Introduction to actuarial science (1st ed.). Wiley. Madsen, D. B. (2021). The actuarial foundations of pension funds (1st ed.). Springer. Merton, R. C., & Bodie, Z. (2020). The design of pension systems: Principles and applications (1st ed.). Cambridge University Press.

FST6094324 Introduction to Microeconomics

Module Name	Microeconomics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094324
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Irma Fauziah, M.Si.
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of explaining (C2) microeconomic activity patterns, analyzing (C4) curves related to economic activities, and presenting (A5) the results both orally and in writing
Module content
Lecture (Class Work) Field of Study in Economics Economic Activity Patterns Economic Issues and Economic System Regulation Demand, Supply, and Market Equilibrium Elasticity of Demand and Supply Applications of Demand and Supply Theory Consumer Behavior Theory: Utility Theory Consumer Behavior Theory: Analysis of Indifference Curves Production Theory and Company Activities Production Cost Theory Perfect Competition Market Monopoly Monopolistic Competition Oligopoly Demand for Factors of Production Wage Determination in the Labor Market Rent, Interest, and Profits Free Markets and Government Policies
Reference: Begg, D., Fischer, S., & Dornbusch, R. (2020). Economics (12th ed.). McGraw-Hill Education. Mankiw, N. G. (2021). Principles of microeconomics (9th ed.). Cengage Learning. Pindyck, R. S., & Rubinfeld, D. L. (2020). Microeconomics (9th ed.). Pearson. McConnell, C. R., Brue, S. L., & Flynn, S. M. (2021). Economics: Principles, problems, and policies (21st ed.). McGraw-Hill Education. Krugman, P., & Wells, R. (2021). Microeconomics (7th ed.). Worth Publishers. Samuelson, P. A., & Nordhaus, W. D. (2020). Economics (20th ed.). McGraw-Hill Education. Parkin, M. (2020). Microeconomics (12th ed.). Pearson. Baumol, W. J., & Blinder, A. S. (2020). Economics: Principles and policy (13th ed.). Cengage Learning. Hubbard, R. G., & O’Brien, A. P. (2021). Microeconomics (7th ed.). Pearson. Colander, D. C. (2020). Microeconomics (10th ed.). McGraw-Hill Education.

FST6094307 Introduction to General Insurance

Module Name	Introduction to General Insurance
Module level, if applicable	Undergraduate
Module Identification Code	FST6094307
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Dhea Urfiba Zulkifli, M.Si
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Introduction to Mathematical Finance
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students are able to analyze financial risks in both the banking and non-banking industries.
Module content
Lecture (Class Work) Claim-frequency Distribution Claim-severity Distribution Aggregate-loss Models Risk Measures Ruin Theory
Reference: Doherty, N. A. (2020). Financial risk management: A practitioner’s guide to managing market and credit risk (2nd ed.). Wiley. Zanjirani Farahani, R., & Hekmatfar, M. (2021). Risk management and insurance (2nd ed.). Springer. Young, R. A., & Linder, R. R. (2021). Risk management for enterprises and individuals (3rd ed.). Springer. Gray, C., & Long, J. (2021). General insurance: A guide to risk management and pricing (3rd ed.). Pearson. Cummins, J. D., & Weiss, M. A. (2020). Risk management and insurance (11th ed.). Pearson. Braithwaite, A. (2020). Principles of risk management and insurance (14th ed.). Pearson. Chan, F. T., & Lai, K. H. (2021). Handbook of insurance (2nd ed.). Springer. Stone, D. H. (2021). Insurance theory and practice (5th ed.). Routledge. Schwartz, D., & Trieschmann, J. (2020). The theory of insurance (4th ed.). Wiley. Hull, J. C. (2021). Risk management and financial institutions (5th ed.). Wiley.

FST6094309 Introduction to Sharia Insurance

Module Name	Introduction to Sharia Insurance
Module level, if applicable	Undergraduate
Module Identification Code	FST6094309
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Mahmudi, M.Si
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students are able to construct and simulate sharia insurance model.
Module content
Lecture (Class Work) Definition and principles of Sharia insuranceDifferences between Sharia Insurance and Conventional InsuranceWakala ModelMudharabah ModelMudharabah – Wakala ModelReturn of InversmentSimulation of Sharia Insurance Models
Reference: Kwon, W. J., & Bae, S. Y. (2020). Shariah insurance: Theories and practices. Springer. Abdul-Rahman, A. (2021). Introduction to Islamic insurance: Principles, practices, and models. Routledge. Ali, S. S., & Mohamed, F. H. (2022). Islamic insurance: A practical guide to Takaful. Wiley. Obaidullah, M. (2020). Islamic finance: A guide to Shariah-compliant business models and instruments. Cambridge University Press. Mollah, M. S. (2021). Takaful and Islamic insurance: A guide for risk management and insurance professionals. Palgrave Macmillan. Saleh, N. M., & Sulaiman, M. (2022). Islamic risk management and Shariah insurance models. Springer. Hassan, M. K., & Shamsuddin, A. (2020). Islamic financial markets and institutions: An introduction to the theory and practice of Islamic insurance. Routledge. Ismail, R. (2021). Shariah-compliant financial models: The Takaful approach. Wiley. Khan, F. (2021). Principles and practices of Islamic insurance (Takaful). Springer. Ahmed, H. (2020). The fundamentals of Islamic insurance: Concepts and practices. Springer.

FST6094316 Analysis of Social Media

Module Name	Analysis of Social Media
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094316
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dr. Taufik Sutanto
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Exploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
These learning outcomes aim to provide students with a well-rounded skill set that can be applied in professional settings where social media analysis is valuable, such as marketing, business intelligence, research, and strategic planning.
Module content
Lecture (Class Work) Introduction and the Importance of Social Media Analysis Understanding Major Social Media Platforms Data Collection Methods Data Analysis Techniques Social Media Monitoring and Listening Ethical Considerations Reporting and Communication Guest Speakers and Industry Applications Student Presentations and Projects Future Trends and Conclusion.
Reference: Alalwan, A. A., & Dwivedi, Y. K. (2020). Social media marketing: Theories and applications. Springer. Li, H., & Wang, Y. (2022). Analyzing social media: A guide to research methods. Routledge. Murthy, V. (2021). Social media data mining and analytics: Techniques and applications. Wiley. Sharma, G., & Singh, M. (2020). Social media analytics: Techniques and applications for business and research. Springer. Zhang, X., & Zhao, L. (2022). The art of social media analysis: A practical guide to marketing insights. Routledge. Hennig-Thurau, T., & Wiertz, C. (2020). Social media strategy: A practical approach to social media analytics. Springer. Michaelidou, N., & de Chernatony, L. (2020). Social media and digital marketing: Strategies for business success. Palgrave Macmillan. Sullivan, J. L., & Yadav, M. S. (2021). Social media for business and research: Theoretical and practical perspectives. Palgrave Macmillan. Chan, S., & Paolillo, J. (2021). Social media analytics: Tools, techniques, and best practices. Wiley. Kaplan, A. M., & Haenlein, M. (2021). Social media: Insights, trends, and analysis. Springer.

FST6094314 Time Series Analysis

Module Name	Time Series Analysis
Module level, if applicable	Undergraduate
Module Identification Code	FST6094314
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Linear Model
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students should become skillful in analyzing and modeling of stochastic process ARIMA(p,d,q) model. Considered methods and models should be mastered by practice using real-world data and implemented with statistical software such as RStudio.
Module content
Lecture (Class Work) Motivating examples and characteristics of time series data Stationarity Autocovariance, autocorrelation, and partial autocorrelation function Stationary and non-stationary time series: AR(p), MA(q), ARMA(p,q), ARIMA(p,d,q) Model identification: ACF, PACF, EACF, Information Criteria Model estimation: Method of moment, LS, ML Model diagnosis: analysis of residual and overparameterization Cross validation in time series model Forecasting
Reference: Brockwell, P. J., & Davis, R. A. (2021). Introduction to time series and forecasting (4th ed.). Springer. Hyndman, R. J., & Athanasopoulos, G. (2020). Forecasting: principles and practice (2nd ed.). OTexts. Shumway, R. H., & Stoffer, D. S. (2020). Time series analysis and its applications: With R examples (4th ed.). Springer. Tsay, R. S. (2021). Analysis of financial statements and time series analysis (2nd ed.). Wiley. Hamilton, J. D. (2020). Time series analysis (2nd ed.). Princeton University Press. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2022). Time series analysis: Forecasting and control (5th ed.). Wiley. Lütkepohl, H., & Krätzig, M. (2021). Applied time series econometrics (2nd ed.). Cambridge University Press. Wei, W. W. S. (2020). Time series analysis: Univariate and multivariate methods (2nd ed.). Pearson. Pankratz, A. (2020). Forecasting with dynamic regression models (2nd ed.). Wiley. Chatfield, C. (2021). The analysis of time series: An introduction (7th ed.). CRC Press.

FST6094315 Biostatistics

Module Name	Biostatistics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094315
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics & Linear Model
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to: Understand the role of biostatistics in public health or medical studies Understand the principle of various study designs, and explain their advantages and limitations Identify appropriate tests to perform hypothesis testing and to fit relevant models to address quantitative problems from public health or medical studies, and interpret the output adequately
Module content
Lecture (Class Work) Study designs Type of study designs Classification clinical trials Sample size determination Comparing one and two groups (continuous outcome): one sample population mean, paired sample t-test, independent sample t-test Comparing more than two groups (continuous outcome): one-way ANOVA & two-way ANOVA Comparing two groups (categorical outcome): Chi-square test, McNemar’s test, OR, RR Introduction to longitudinal data analysis: random intercept model, random intercept and slope model Introduction to survival analysis: Survival and Hazard functions, Kaplan-Meier
Reference: Sullivan, L. M. (2021). Essentials of biostatistics in public health (3rd ed.). Jones & Bartlett Learning. Bland, M. (2020). An introduction to medical statistics (4th ed.). Oxford University Press. Glantz, S. A., & Slinker, B. K. (2021). Primer of biostatistics (8th ed.). McGraw-Hill Education. Kelsey, J. L., & Whittemore, A. S. (2020). Methods in observational epidemiology (3rd ed.). Oxford University Press. Pagano, M., & Gauvreau, K. (2021). Principles of biostatistics (3rd ed.). Cengage Learning. Kleinbaum, D. G., & Klein, M. (2020). Survival analysis: A self-learning text (4th ed.). Springer. Lee, E. T., & Wang, J. W. (2020). Statistical methods for survival data analysis (5th ed.). Wiley. Hosmer, D. W., Lemeshow, S., & May, S. (2021). Applied survival analysis: Regression modeling of time-to-event data (2nd ed.). Wiley. Monti, M. C., & Oliviero, C. (2021). Biostatistics for medical students (3rd ed.). Springer. Rothman, K. J., Greenland, S., & Lash, T. L. (2021). Modern epidemiology (4th ed.). Wolters Kluwer.

FST6094327 High Performance Computing

Module Name	High Performance Computing
Module level, if applicable	Undergraduate
Module Identification Code	FST6094327
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dr. Taufik Sutanto
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Exploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
These learning outcomes collectively aim to equip students with the knowledge and skills necessary to design, implement, and optimize parallel algorithms for high-performance computing environments.
Module content
Lecture (Class Work) Understanding Parallel Architectures Parallel Programming Models Algorithm Design for Parallel Computing Parallelization Techniques High-Performance Computing Tools and Libraries Performance Analysis and Profiling Distributed Systems and Clusters Message Passing Interface (MPI) Load Balancing and Scalability Optimization Strategies Parallel I/O and Storage
Reference: Pacheco, P. (2020). Parallel programming with MPI (2nd ed.). Morgan Kaufmann. Gropp, W., Lusk, E., & Thakur, R. (2021). Using MPI: Portable parallel programming with the Message-Passing Interface (4th ed.). MIT Press. Quinn, M. J. (2021). Parallel programming in C with MPI and OpenMP (2nd ed.). McGraw-Hill Education. Hennessy, J. L., & Patterson, D. A. (2020). Computer architecture: A quantitative approach (6th ed.). Morgan Kaufmann. McCool, M., Reinders, J., & Robison, A. (2021). Structured parallel programming: Patterns for efficient computation (2nd ed.). Elsevier. Dongarra, J., & Bader, D. (2020). High-performance computing: Paradigm and infrastructure (2nd ed.). Springer. Cameron, R. D., & Swaminathan, V. (2020). Advanced parallel programming for modern high-performance computing systems (1st ed.). Wiley. Foster, I. (2021). Design and implementation of high-performance computing systems (2nd ed.). Wiley. Kaiser, H. (2021). Parallel and distributed computing: A survey (1st ed.). Springer. Saini, H., & Bhagat, P. (2020). High performance computing: Modern approaches and challenges (1st ed.). CRC Press.

FST6094310 Selective Capita

Module Name	Selective Capita
Module level, if applicable	Undergraduate
Module Identification Code	FST6094310
Semester(s) in which the module is taught	6
Person(s) responsible for the module
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Midterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Mastering specialized topics in data science, actuarial science, or pure and applied mathematics.
Module content
Lecture (Class Work) Specialized topics in data science, actuarial science, or pure and applied mathematics.
Recommended Literatures Books and articles on specific topics in data science, actuarial science, or pure and applied mathematics.

FST6094322 Advanced Actuarial Mathematics

Module Name	Advanced Actuarial Mathematics
Module level, if applicable	Undergraduate
Module Identification Code	FST6094322
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Irma Fauziah, M.Sc
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	Has taken Introduction to Actuarial Mathematics course
Media employed	Classical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to solve (C4) problems related to modeling net premiums and premium reserves for multi-life insurance and able to present (A5) the results.
Module content
Lecture (Class Work) Joint Life Status Last Survivor Status Multi-Life Life Insurance Multi-Life Life Annuity Premiums and Reserves for Multi-Life Insurance Makeham and Gompertz Mortality Laws Uniform Distribution of Death (UDD) Law Simple and Multiple Contingency Functions Simple Contingency Insurance and Annuities Reserves for Simple Contingency Insurance
Reference: Dickson, D. C. M., Hardy, M. R., & Waters, H. R. (2020). Actuarial mathematics for life contingent risks (2nd ed.). Cambridge University Press. Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (2019). Actuarial mathematics (2nd ed.). Society of Actuaries. Müller, M., & Madsen, M. (2021). Advanced actuarial mathematics: Theories and practical applications. Wiley. Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2020). Loss models: From data to decisions (5th ed.). Wiley. McCutcheon, J. J., & Scott, D. (2021). The mathematics of insurance: Contingencies and life insurance mathematics. Springer. Promislow, S. D. (2020). Mathematics of actuarial modeling: Life insurance and pension mathematics. Springer. Haberman, S., & Renshaw, A. E. (2021). Actuarial science: Theory and methodology (3rd ed.). CRC Press. Nielsen, A. P., & Skov, T. B. (2021). Actuarial mathematics for life and health insurance (1st ed.). Wiley. Wang, S., & Zhang, R. (2020). Contingency theory and actuarial science: An advanced approach. Springer. Fong, S. L., & Keng, C. T. (2020). Advanced actuarial mathematics: Life and multi-life models. Wiley.

FST6091911 Natural Language Processing

Module Name	Natural Language Processing
Module level, if applicable	Undergraduate
Module Identification Code	FST6091911
Semester(s) in which the module is taught	5
Person(s) responsible for the module	Muhaza Liebenlito, M.Si.
Language	Indonesian
Relation in Curriculum	Compulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	– Data Mining – Basic Programming
Media employed	LMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessment	Assignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
On completion of this subject the student is expected to: Identify basic challenges associated with the computational modelling of natural language; Understand and articulate the mathematical and/or algorithmic basis of common techniques used in natural language processing; Implement relevant techniques and/or interface with existing libraries; Carry out end-to-end research experiments, including evaluation with text corpora as well as presentation and interpretation of results; Critically analyse and assess text-processing systems and communicate criticisms constructively.
Module content
Lecture (Class Work) 1. Text classification and unsupervised topic discovery 2. Vector space models for natural language semantics 3. Structured prediction for tagging 4. Syntax models for parsing of sentences and documents 7. N-gram language modelling 8. Machine translation 9. Deep learning for NLP
Reference: Jurafsky, D., & Martin, J. H. (2021). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (3rd ed.). Pearson. Goldberg, Y. (2020). Neural network methods for natural language processing. Morgan & Claypool Publishers. Manning, C. D., & Schütze, H. (2019). Foundations of statistical natural language processing. MIT Press. Chin, F., & Barzilay, R. (2021). Introduction to natural language processing. MIT Press. Koller, D., & Maletti, T. (2022). Practical natural language processing: A comprehensive guide to applying deep learning models to your text. O’Reilly Media. Goldberg, Y., & Hinton, G. (2020). Deep learning for natural language processing: A hands-on guide to building real-world NLP applications. O’Reilly Media. Ruder, S. (2021). Deep learning for natural language processing: Advanced techniques and applications. Springer. Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, Ł., & Polosukhin, I. (2019). Attention is all you need. MIT Press. Bird, S., Klein, E., & Loper, E. (2021). Natural language processing with Python: Analyzing text with the Natural Language Toolkit. O’Reilly Media.

FEB6084202 Introduction to Macroeconomics

Module Name	Introduction to Macroeconomics
Module level, if applicable	undergraduate
Module Identification Code	FEB6084202
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Irma Fauziah, M.Sc.
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of explaining (C2) macroeconomic activity patterns, analyzing (C4) curves related to aggregate demand and aggregate supply, and articulating (A5) their findings in both oral and written language.
Module content
Lecture (Class Work) Introduction to MacroeconomicsEconomic Activity DeterminationMoney and Financial InstitutionsEconomic Policy in Closed and Open EconomiesEconomic Growth Concepts and Determinants
Reference: Mankiw, N. G. (2021). Macroeconomics (10th ed.). Worth Publishers. Blanchard, O., & Johnson, D. R. (2020). Macroeconomics (7th ed.). Pearson. Krugman, P., & Wells, R. (2020). Macroeconomics (6th ed.). Worth Publishers. Abel, A. B., Bernanke, B. S., & Croushore, D. (2021). Macroeconomics (9th ed.). Pearson. Carlin, W., & Soskice, D. (2020). Macroeconomics: Imperfections, institutions, and policies (5th ed.). Oxford University Press. Mishkin, F. S. (2021). The economics of money, banking, and financial markets (12th ed.). Pearson. Case, K. E., Fair, R. C., & Oster, S. M. (2020). Principles of economics (12th ed.). Pearson. Hubbard, R. G., & O’Brien, A. P. (2020). Macroeconomics (7th ed.). Pearson. Dornbusch, R., Fischer, S., & Startz, R. (2021). Macroeconomics (13th ed.). McGraw-Hill. Pugel, T. A. (2020). International economics (17th ed.). McGraw-Hill.

FST6094320 Introduction to Actuarial Computing

Module Name	Introduction to Actuarial Computing
Module level, if applicable	undergraduate
Module Identification Code	FST 6094320
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dhea Urfina Zulkifli, M.Si
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	Differentiated learning & inquiry-based learning, class discussion, structured activities (homework, quizzes).
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the introductory material of R software, survival models, life insurance, life annuities, and life insurance premiums using the R software.
Module content
Lecture (Class Work) Introduction to R software Survival model Life insurance Life annuity\ Life insurance premium
Reference: Baillie, R. T., & Bollerslev, T. (2020). R for actuarial science: A practical guide. Springer. Haberman, S., & Taylor, S. (2021). Actuarial mathematics for life contingent risks (2nd ed.). Cambridge University Press. Pitt, M., & Wilson, J. (2021). Introduction to R for actuarial science and finance. Wiley. Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (2020). Actuarial mathematics (2nd ed.). Society of Actuaries. Shi, Y., & Vanden, H. (2020). Survival analysis with R: Practical applications and case studies. Springer. Oakes, D. (2021). The basics of actuarial mathematics and R: Tools for data analysis and risk modeling. Wiley. Cairns, A. J. G., & Blake, D. (2021). Modelling mortality with R: A practitioner’s guide. Wiley. Sweeney, D., & Zick, C. D. (2020). Life insurance mathematics using R. Springer. Clarke, R. (2020). R for financial modeling and actuarial analysis. Wiley. Dufresne, M. (2021). R programming for actuarial science: An introduction to data analysis and modeling. Springer.

FST6094317 Introduction to Financial Computing

Module Name	Introduction to Financial Computing
Module level, if applicable	undergraduate
Module Identification Code	FST 6094317
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dhea Urfina Zulkifli, M.Si.
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	Project-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the introductory material of programming with R software, interest rates, investment yield rates, annuities, general annuities, loan repayments, bonds, spot rates, and forward rates, as well as asset-liability management, duration, and immunization using R software.
Module content
Lecture (Class Work) Introduction to programming with R software Interest rates Investment yield rates Annuities General annuities Loan repayments Bonds Spot rates and forward rates Asset-liability management, Duration, Immunization
Reference: Tushar, P., & Krishnamurthy, A. (2021). Financial computing with R: A beginner’s guide. Wiley. Clewlow, L., & Strickland, C. (2020). Financial engineering with R: A hands-on guide to financial modeling and risk management. Springer. Brown, D., & Reitz, S. (2020). Quantitative finance with R: A beginner’s guide to financial modeling and risk management. CRC Press. Rebonato, R. (2020). The R book for financial applications. Wiley. Sierl, S. (2021). R for finance: A practical guide to financial modeling. Springer. Dowd, K. (2021). An introduction to financial markets with R. Wiley. Jorion, P. (2020). Value at risk: The new benchmark for managing financial risk (5th ed.). McGraw-Hill. Lambert, C., & Wester, R. (2020). Financial risk management with R: A hands-on guide to understanding risk. Springer. Verma, S., & Dhal, S. (2021). Advanced financial modeling with R: Tools and techniques for the modern risk manager. Wiley. Vasicek, O., & Zhou, C. (2020). Introduction to financial markets and institutions using R. Springer.

FST6094328 Introduction to Statistical Computing

Module Name	Introduction to Statistical Computing
Module level, if applicable	Undergraduate
Module Identification Code	FST6094328
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Madona Yunita Wijaya
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Linear Model
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	Assignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Students are able to use computational and graphic approaches to solve statistical problems. They can manipulate or modify data, present data using graphics, design and conduct simple Monte Carlo experiments, and can use resampling methods such as Bootstrap.
Module content
Lecture (Class Work) Introduction to R Dynamic and Reproducible Reporting with R Markdown Data Visualization Data Manipulation Inferential Statistics Random Number Generation Simulation of Distribution Models Monte Carlo Simulation Bootstrap Permutation Methods Jackknife Cross-validation
Reference: Wickham, H., & Grolemund, G. (2019). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media. Peng, R. D. (2021). R programming for data science. Leanpub. Kabacoff, R. I. (2019). R in action: Data analysis and graphics with R (2nd ed.). Manning Publications. Adler, D., & Geenen, D. (2020). R graphics cookbook: Practical recipes for visualizing data. O’Reilly Media. R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Gelman, A., & Hill, J. (2020). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. Field, A., Miles, J., & Field, Z. (2021). Discovering statistics using R. Sage Publications. Izenman, A. J. (2020). Modern multivariate statistical techniques: Regression, classification, and manifold learning. Springer. Crawley, M. J. (2021). Statistics: An introduction using R (3rd ed.). Wiley. Efron, B., & Tibshirani, R. J. (2019). An introduction to the bootstrap. CRC Press.

FST6094318 Introduction to Insurance Company Operation

Module Name	Introduction to Insurance Company Operations
Module level, if applicable	undergraduate
Module Identification Code	FST6094318
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Irma Fauziah, M.Si.
Language	Indonesian
Relation in Curriculum	Actuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hours	Differentiated learning & inquiry-based learning, class discussion, structured activities (homework, quizzes).
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	None
Media employed	Classical teaching tools with white board and PowerPoint presentation
Forms of assessment	Midterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
Capable of classifying (C3) the operations of conventional life insurance companies and Sharia-compliant life insurance, and able to present (A5) the results in both spoken and written language.
Module content
Lecture (Class Work) Understanding Life Insurance Risk and Insurance Insurance Contracts Insurance Policies Term Life Insurance Whole Life and Endowment Life Insurance New Generation Savings Insurance Unit Linked Insurance Products Pension and Annuity Programs Technical Aspects of Life Insurance Sharia Insurance Theory and Scholars’ Opinions on Insurance The Phenomenon of Usury (Riba) and Interest in the Operational System of Life Insurance in Eliminating Ambiguity (Gharar), Gambling (Maisir), and Usury (Riba) Operational Aspects of Insurance Companies Restructuring and Formation of Life Insurance Companies and Organizational Structures of Insurance Companies Marketing Strategies and Product Distribution Activities Life Insurance Underwriting Actuarial Functions Life Insurance Claim Administration
Reference: Harrington, S. E., & Niehaus, G. R. (2020). Risk management and insurance (2nd ed.). McGraw-Hill Education. Vaughan, E. J., & Vaughan, T. (2020). Fundamentals of risk and insurance (12th ed.). Wiley. Rejda, G. E., & McNamara, M. J. (2021). Principles of risk management and insurance (14th ed.). Pearson. Outreville, J. F. (2019). Theory and practice of insurance (2nd ed.). Springer. Eling, M., & Luhnen, M. (2021). The economics of insurance intermediaries. Springer. Abbott, J., & Zarebski, J. (2020). Sharia-compliant insurance: A guide to the Islamic insurance industry. Wiley. Holsboer, J. L., & Pfortmueller, A. (2020). The structure of the insurance industry and its regulation. Springer. Mohamad, A. R., & Sanusi, Z. M. (2020). Sharia insurance: A theoretical and practical approach. Universiti Putra Malaysia Press. Buckham, B. D., & Crabb, G. D. (2021). Introduction to insurance (5th ed.). Cengage Learning. Hillier, D., & McColgan, P. (2020). Insurance: A business perspective. Routledge.

FST6094108 Multivariate Statistic

Module Name	Multivariate Statistic
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094108
Semester(s) in which the module is taught	3
Person(s) responsible for the module	Dr. Suma Inna, M.Si Mahmudi, M.Si
Language	Indonesian
Relation in Curriculum	Compulsory course
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites	Calculus II
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Able to explain (C1) (A5) the concepts of parametric functions and scalar functions both verbally and in writing. Capable of solving problems (C4) related to partial derivatives of scalar functions and presenting the results logically and systematically, both verbally and in writing. Capable of solving problems (C4) related to vector functions and presenting the results logically and systematically, both verbally and in writing. Capable of solving problems (C4) related to double and triple integrals, line integrals, and surface integrals of multivariable functions, both verbally and in writing. Able to demonstrate (C4) the relationship between double integrals and line integrals, both verbally and in writing.
Module content
Lecture (Class Work) Parametric Functions: Limits and Continuity of Parametric Functions; Integral and Arc Length of Parametric Functions. Scalar Functions: Height Curves; Limits and Continuity; Partial Derivatives and Gradient Vectors; Differentiability; Total Differentials; Chain Rule; Directional Derivatives; Implicit Differentiation; Extrema of Functions; Lagrange Method Vector Functions: Divergence and Curl; Conservative Vector Fields; Chain Rule; Jacobian Matrix; Inverse of Vector Functions. Multiple Integrals: Double Integrals; Triple Integrals; Coordinate Transformation in Multiple Integrals (Polar, Curvilinear, Cylindrical, and Spherical Coordinates). Line Integrals: Line Integrals of Vector Fields; Relationship between Line Integrals
Reference: Abdi, H., & Williams, L. J. (2020). Multivariate analysis for the behavioral sciences. Routledge. Anderson, T. W., & Fang, K.-T. (2021). Introduction to multivariate statistical analysis (4th ed.). Wiley. Härdle, W., & Simar, L. (2020). Applied multivariate statistical analysis (5th ed.). Springer. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd ed.). Springer. Johnson, R. A., & Wichern, D. W. (2022). Applied multivariate statistical analysis (7th ed.). Pearson. Rencher, A. C., & Christensen, W. F. (2019). Methods of multivariate analysis (3rd ed.). Wiley. Izenman, A. J. (2020). Modern multivariate statistical techniques: Regression, classification, and manifold learning (2nd ed.). Springer. Mardia, K. V., Kent, J. T., & Bibby, J. M. (2021). Multivariate analysis (3rd ed.). Academic Press. Lattin, J., Carroll, J. D., & Green, P. E. (2020). Analyzing multivariate data (2nd ed.). Thomson Learning. Seber, G. A. F. (2021). Multivariate observations (2nd ed.). Wiley.

 

FST6094319 Sampling Techniques and Experimental Design

Module Name	Sampling Technique and Experimental Design
Module level, if applicable	Undergraduate
Module Identification Code	FST6094319
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Ary Santoso
Language	Indonesian
Relation in Curriculum	Elective course for undergraduate program in Mathematics
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Elementary Statistics and Mathematical Statistics I
Media employed	Classical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment	·        Assignments (including quizzes and group project): 40% ·        Midterm exam: 30% ·        Final exam: 30%
Intended Learning Outcome: – Identify and explain the sample survey design dan experimental design –           Apply the sample survey and experimental design methods on real cases –           Apply the data preparation, processing, and analyzing
Module content
Sampling Technique The teaching materials consist of planning a survey, conducting a survey, methods of collecting survey data, selecting a sample survey, determining a sample size, analyzing survey data Experimental Design The teaching materials consist of planning an experiment, conducting an experiment, methods of collecting data, analyzing design experimental data Recommended Literatures
Reference: Cochran, W. G. (2021). Sampling techniques (4th ed.). Wiley. Montgomery, D. C. (2020). Design and analysis of experiments (10th ed.). Wiley. Lohr, S. L. (2021). Sampling: Design and analysis (3rd ed.). Cengage Learning. Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2020). Applied linear statistical models (5th ed.). McGraw-Hill. Valliant, R., Dever, J. A., & Kreuter, F. (2020). Practical tools for designing and weighting survey samples (2nd ed.). Springer. Dean, A., Voss, D., & Draguljić, D. (2021). Design and analysis of experiments (3rd ed.). Springer. Särndal, C. E., Swensson, B., & Wretman, J. (2021). Model assisted survey sampling (2nd ed.). Springer. Imdadullah, M., Aslam, M., & Almas, A. (2020). Design of experiments for engineers and scientists (2nd ed.). Wiley. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2020). Experimental and quasi-experimental designs for generalized causal inference (2nd ed.). Houghton Mifflin. Alwin, D. F. (2021). Margins of error: A study of reliability in survey measurement (2nd ed.). Wiley.

FST6094326 Number Theory

Module Name	Number Theory
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094326
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Mahmudi, M.Si
Language	Indonesian
Relation in Curriculum	Pure mathematics  specialization courses for the mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Discrete mathematics
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to disibility, greatest common divisors, congruences, and multiplicative functions, and be able to present (A5) the results.
Module content
Lecture (Class Work) Binomial theorem and Disibility Greatest common divisors Euclidean algorithm Linear congruences and systems of linear congruences Application of congruences Wilson’s theorem and Fermat’s theorem Multiplicative functions
Reference: Burton, D. M. (2020). Elementary number theory (8th ed.). McGraw-Hill. Jones, G. A., & Jones, J. M. (2021). Elementary number theory (2nd ed.). Springer. Nathanson, M. B. (2021). Elementary methods in number theory (2nd ed.). Springer. Niven, I., Zuckerman, H. S., & Montgomery, H. L. (2021). An introduction to the theory of numbers (7th ed.). Wiley. LeVeque, W. J. (2020). Fundamentals of number theory (2nd ed.). Dover Publications. Dudley, U. (2021). Elementary number theory (2nd ed.). Dover Publications. Silverman, J. H. (2020). A friendly introduction to number theory (5th ed.). Pearson. Rosen, K. H. (2021). Elementary number theory and its applications (7th ed.). Pearson. Everest, G., & Ward, T. (2021). An introduction to number theory (2nd ed.). Springer. Stillwell, J. (2020). Elements of number theory (2nd ed.). Springer.

FST6094121 Linear Algebra

Module Name	Linear Algebra
Module level, if applicable	Undergraduate
Module Identification Code	FST 6094326
Semester(s) in which the module is taught	6
Person(s) responsible for the module	Dr. Gustina Elfiyanti, M.Si
Language	Indonesian
Relation in Curriculum	Pure mathematics  specialization courses for the mathematics undergraduate program
Teaching methods, Contact hours	The course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
Workload	• Lecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 45 min x 14 wks = 31.5 h  • Independent study: 3 x 45 min  x 14 wks = 31.5 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 103 hours
Credit points	3 Credit Hours ≈ 3.433 ECTS
Admission and examination requirements	Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites	Calculus 1 and Discrete mathematics
Media employed	a whiteboard and and projector
Forms of assessment	Midterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to solve problems (C4) related to systems of linear equations, matrices, vector spaces, and linear transformations, and articulate their results (A5) in both oral and written forms.
Module content
Lecture (Class Work) Linear Equation Systems and Matrices Determinant Euclidean Vector Space General Vector Space Eigenvalues and Eigenvectors Dot Product Diagonalization and Quadratic Forms\ Linear Transformations
Reference: Axler, S. (2021). Linear algebra done right (4th ed.). Springer. Lay, D. C., Lay, S. R., & McDonald, J. J. (2020). Linear algebra and its applications (6th ed.). Pearson. Strang, G. (2020). Introduction to linear algebra (6th ed.). Wellesley-Cambridge Press. Bretscher, O. (2021). Linear algebra with applications (6th ed.). Pearson. Hefferon, J. (2020). Linear algebra. Open Source Text. Leon, S. J. (2020). Linear algebra with applications (10th ed.). Pearson. Kolman, B., & Hill, D. R. (2021). Elementary linear algebra with applications (10th ed.). Pearson. Poole, D. (2021). Linear algebra: A modern introduction (5th ed.). Cengage Learning. Friedberg, S. H., Insel, A. J., & Spence, L. E. (2020). Linear algebra (5th ed.). Pearson. Lipschutz, S., & Lipson, M. (2020). Schaum’s outline of linear algebra (6th ed.). McGraw-Hill Education.