Students in the Computer Science (CS) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing algorithms or computational theories that can address current and real-world problems.
Compulsory courses for the Computer Science (CS) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5110 |
Algorithm Design
- Review basic concepts of algorithms: time complexity, data structures, heaps & disjoint sets data structures
- Techniques based on recursion: induction, divide & conquer, dynamic programming
- First cut techniques: greedy, graph traversal
- Complexity of problems: NP-complete problems, computational complexity, lower bounds
- Coping with hardness: backtracking, randomized algorithms, approximation algorithm
- Iterative improvement for domain-specific problems: network flow, matching
- Techniques in Computational Geometry: Geometric sweeping,Voronoi diagrams
|
3 |
0 |
| 2 |
IF5111 |
Mathematics for Computer Science
- Proofs
- Structures
- Counting
- Statistics and Probability
- Recurrences
- Algorithm complexity
|
3 |
0 |
| 3 |
IF5210 |
Compiler Design
- Lexical analysis
- Syntax analysis
- Scopes and symbol tables
- Interpretation
- Type checking
- Intermediate code generation
- Machine code generation
- Register allocation
- Functions
- Data flow analysis and optimisation
- Optimisation for loops
|
3 |
0 |
| 4 |
IF5112 |
Advanced Distributed Systems
- Model sistem terdistribusi
- Failure detectors
- reliable delivery
- atomic & reliable broadcast
- shared memory model
- consensus
- Distributed commitment protocol & atomic transactions
- Group membership
- View synchrony
- Cloud computing & virtualization
- Distributed ledger & blockchain
|
3 |
0 |
Students in the Software Engineering (SE) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing large-scale software products that are reliable, innovative, and maintainable.
Compulsory courses for the Software Engineering (SE) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5120 |
Software Requirement Engineering
- Fundamentals of software requirements
- Requirements engineering process
- Requirements elicitation
- Requirements analysis
- Requirements specification
- Requirements validation
- Practical considerations
|
3 |
0 |
| 2 |
IF5121 |
Software Design
- Fundamentals of software design
- Software architecture
- Design patterns
- User interface design
- Analysis and evaluation of software design quality
- Software design notations
- Software design strategies and methods
- Software design tools
|
3 |
0 |
| 3 |
IF5220 |
Software Quality
- Fundamentals of software quality
- Software quality management process
- Practical considerations
- Software quality tools
- Fundamentals of software testing
- Levels of testing
- Testing techniques
- Testing-related metrics
- Testing process
- Software testing tools
- Specification languages
- Program refinement and derivation
- Verifikasi Formal
- Logical inference
|
3 |
0 |
| 4 |
IF5221 |
Software Product Innovation
- Introduction to software product innovation
- Topic and problem identification
- Market needs identification and analysis
- System requirements identification and analysis
- Requirements modeling
- Software product and quality planning
- Commercialization planning
- Proposal presentation
- Software product development implementation
- Presentation and demo
|
1 |
3 |
| 5 |
IF6120 |
Software Evolution
- SCM process management
- Software configuration identification
- Software configuration control
- Software configuration status
- Software configuration audit
- Software release management and delivery
- SCM tools
- Fundamentals of software maintenance
- Issues in software maintenance
- Software maintenance process
- Software maintenance techniques
- Software maintenance tools
|
3 |
0 |
Students in the Media and Mobile Technology (MMT) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing concepts, architectures, and technologies for media and mobile devices, as well as developing applications for media and mobile devices using principles of human-computer interaction and proper software engineering methodologies.
Compulsory courses for the Media and Mobile Technology (MMT) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5130 |
Software Engineering for Game Domain
- Software engineering for the game domain
- Game design
- Game engine architecture
- Game testing
- Game analytics
- Ethics in game development
|
3 |
0 |
| 2 |
IF5131 |
Multimedia Data Processing and Management
- Introduction to multimedia data and applications
- Review of signal processing (Discrete Fourier Transform and Fast Fourier Transform)
- Review of multimedia data representation
- Automated analysis of multimedia data (preprocessing, feature extraction, recognition, and similarity retrieval)
- Multimedia data management and indexing methods
- Case studies of multimedia data-based applications
|
3 |
0 |
| 3 |
IF5230 |
Mobile Technology and Application
- Introduction to mobile devices
- Activities in mobile application development (requirements analysis, design principles and patterns, user interface (UI) design, technology implementation, testing, and quality)
|
3 |
0 |
| 4 |
IF6130 |
Interactive Media Technology & Application
- Introduction to interactive media
- Introduction to games (concepts, design, process)
- Gameplay
- Game production
- The art of game design
- Game implementation
|
3 |
0 |
Students in the Data Science (DS) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing the management, processing, and interpretation of large and complex datasets for the extraction of important information and knowledge.
Compulsory courses for the Data Science (DS) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5140 |
Machine Learning
- Introduction to Machine Learning
- Supervised Learning
- Neural Networks
- Learning Theory
- Unsupervised Learning
- Reinforcement Learning
|
2 |
0 |
| 2 |
IF5141 |
Data Mining
- Process models for data mining (CRISP-DM)
- Basic concepts of data, statistics, and data visualization; measurement; and data pre-processing
- Basic techniques in pattern mining for frequent patterns, associations, and correlations
- Recall: Classification and cluster analysis using machine learning techniques
- Overview of advanced machine learning techniques for various types of data
- Data mining model evaluation
- Deployment of data mining models
- Case study on building a machine learning model for a specific problem/organization: business understanding, data understanding, data preparation, model building, model evaluation, deployment
|
3 |
1 |
| 3 |
IF5240 |
Business Intelligence and Analytics
- Fundamental concepts of business intelligence and business analytics: data, statistical models, decision-making, and decision support systems
- Management and operations of business analytics: business processes and operations, planning and control, requirements analysis and design, solution evaluation
- Data warehousing: concepts of data warehousing, data integration, data warehouse development, business analytics using data warehouses, data visualization
- Data warehouse technologies: platforms and tools for data warehousing, data integration, reporting, and visualization
- Business analytics using data mining techniques
- Case study on system development using a business intelligence approach and utilizing business analytics
|
3 |
1 |
| 4 |
IF5241 |
Big Data System
- Fundamental concepts of big data
- Big data infrastructure and technologies: big data technology stack, cloud computing, large-scale storage and file systems, NoSQL databases, data processing models: batch, streaming, and parallel
- Organization and management of big data systems: big data algorithms for large-scale data processing, data preprocessing algorithms, data ingestion and visualization, management and operation of big data systems
- Big data system development for data science: platforms and tools for big data analytics, design and development of big data systems and cloud-based systems, operation and management of big data and cloud services, and their relation to enterprise information systems
|
3 |
0 |
| 5 |
IF6097 |
Industrial Internship
- Method implementation
- Scientific report writing
|
3 |
0 |
Students in the Artificial Intelligence (AI) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing concepts, technologies, and applications of artificial intelligence, including appropriate tools as effective and efficient AI solutions.
Compulsory courses for the Artificial Intelligence (AI) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5150 |
Advanced Artificial Intelligence
- Introduction and History of AI - Topics: Introduction to AI and its historical development, Basic concepts of AI
- Intelligent Agent - Topics: Concepts and architecture of intelligent agents, Implementation of intelligent agents
- Decision Making - Topics: Decision-making techniques in AI, Decision-making models
- Heuristic Algorithms - Topics: Concepts and applications of heuristic algorithms, Heuristic search techniques
- Descriptive, Propositional, and Predicate Logic - Topics: Descriptive, propositional, and predicate logic, Applications of logic in AI
- Answer Set and Ontology - Topics: Concepts of answer set, Ontology and its applications in AI
- Linked Data, Semantic Web, and Probabilistic Networks - Topics: Linked data and the semantic web, Bayesian networks and Markov decision networks
|
3 |
0 |
| 2 |
IF5151 |
Mathematics for Artificial Intelligence
- Introduction to Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
- Optimization Methods
- Applied Mathematics for Machine Learning
|
3 |
0 |
| 3 |
IF5250 |
Deep Learning
- Introduction to Deep Learning
- Fundamentals of Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN) and LSTM
- Optimization and Regularization Techniques
- Deep Learning Applications
|
3 |
1 |
| 4 |
IF5251 |
Artificial Intelligence in Production
- Introduction to AI in Production – Challenges and considerations in deploying AI to production, Overview of MLOps and the model development lifecycle, Differences between development and production environments
- Data Management – Data collection, labeling, and storage, Data processing and feature engineering, Data versioning and lineage
- Model Development and Training – Algorithm and model architecture selection, Experimentation and model tracking, Distributed training and hardware acceleration
- Model Evaluation and Validation – Evaluation metrics and cross-validation, Error analysis and model interpretability, Data testing and validation
- Model Deployment – Deployment strategies (shadow, canary, blue-green), Containerization and orchestration, Scaling and performance optimization
- Model Monitoring and Supervision – Monitoring metrics and dashboards, Anomaly and data drift detection, Monitoring data integrity and model quality
- Security and Privacy – Adversarial attacks and defenses, Data encryption and anonymization, Security audits and compliance
- Ethical and Legal Considerations – Bias and fairness in AI, Transparency and accountability, Regulations and industry standards
- Case Studies and Industrial Applications – Case studies of AI implementation across industries, Best practices and lessons learned, Trends and future directions
|
3 |
0 |
| 5 |
IIF6150 |
Trustworthy Artificial Intelligence
- Introduction to Trustworthy AI – Topics: Introduction to the concept of Trustworthy AI, The importance of transparency, fairness, and robustness in AI
- Explainable AI (X-AI) – Concepts and Methods – Topics: Basic concepts of explainable AI, Methods for AI model interpretability
- Interpretability and Explanation Evaluation – Topics: Techniques for interpretability evaluation, Case studies on explainable AI applications
- Fairness in AI – Concepts and Challenges – Topics: Concepts of fairness in AI, Challenges in detecting and mitigating bias
- Techniques for Fairness in AI – Topics: Techniques for detecting bias, Methods for mitigating bias in AI models
- Robustness in AI – Concepts and Methods – Topics: Concepts of robustness in AI, Techniques to improve model robustness
- Ethical and Legal Considerations in AI – Topics: Ethical considerations in AI development, Legal implications of fairness and robustness in AI
- Final Project and Presentation – Topics: Implementation of innovative projects in Trustworthy AI, Project documentation and presentation, Evaluation and discussion of project results
|
3 |
0 |
| 6 |
IF6151 |
Artificial Intelligence Individual Project
- Current topics in Artificial Intelligence issues
|
3 |
0 |
Students in the Cybersecurity (CSec) track are expected to develop research-based knowledge and technology in the field of Informatics, particularly in the development, operation, and testing of cybersecurity systems.
Compulsory courses for the Cybersecurity (CSec) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5161 |
Data and Software Security
- Overview on Traffic, Vulnerability and Malware Analysis
- Access Control Enhancement to deal with malicious and buggy software
- Usable Integrity Protection
- User Authentication
- Virtual Private Databases
- Overview of Public-Key Cryptography
- Preventing SQL Injection Attacks
- Vulnerability Assessment and Management
- Code Inspection for Finding Security Vulnerabilities and Exposures
- Architectural Risk Analysis
- Penetration Testing, Concolic Testing
- Risk-Based Security Testing and Verification
- Withstanding adversarial tactics and techniques
|
3 |
0 |
| 2 |
IF6160 |
System Security & Privacy
- Cybersecurity and privacy regulations
- Security Building Block
- Cyber-Physical System Engineering
- Management & Incident
- Legal Issues & Ethics
- Malware
|
3 |
0 |
| 3 |
IF5260 |
Digital Forensics
- Introduction to Digital Forensics
- Computer crime and Legal issues
- Digital forensic tools
- Investigatory process
- Analysis of evidence
- Presentation of results
|
3 |
0 |
| 4 |
IF5160 |
Cybersecurity Operations
- Security concepts in organizations
- Network security
- Security operations
- Threat hunting
|
3 |
0 |
| 5 |
IF6161 |
Cybersecurity Individual Project
- Current topics in cybersecurity issues
|
3 |
0 |
Students in the Information Systems (IS) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing Information Systems governance at the enterprise level to maximize system potential and minimize risks through the efficient use of Information Technology resources.
Compulsory courses for the Information Systems (IS) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5170 |
Digital Strategy
- Strategic thinking
- Predicting the direction of IT development
- Information technology adoption
- Change management and policy development
- Management and governance
- IT governance model
- Maturity and capability maturity models
|
3 |
0 |
| 2 |
IF5171 |
System Thinking
- Philosophy of systems
- Basic principles of systems
- Sociocultural systems
- Development
- Systems methodology
- Operational thinking
- Design thinking
|
3 |
0 |
| 3 |
IF5270 |
Applied Artificial Intelligence for Enterprise
- Artificial Intelligence
- Business Value of AI
- Leveraging Business Value Chain
- Development Methodology: CRISP-DM
|
3 |
1 |
| 4 |
IF5271 |
Information System Sustainability
- Sustainability concepts
- Regulations
- Green in OS
- Green by IS
- Product longevity
- Data center Design
- Software Optimization
- Power Management
- Material Recycling
|
3 |
0 |
| 5 |
IF6170 |
Data Governance
- Information and the role of data for organizations
- Data principles
- Data policies and procedures
- Data organizational structure
- Data privacy
- Data sharing
- Data Protection
|
3 |
0 |
Students in the Information Technology (IT) track are expected to develop research-based knowledge and technology in the field of Informatics by designing and developing the analysis, modeling, design, construction, and advancement of information technology-based systems.
Compulsory courses for the Information Technology (IT) track:
| No |
Code |
Matakuliah |
Credits |
P |
| 1 |
IF5180 |
Digital Twin Technology
- Digital Twin Concept
- Digital Twin Framework
- Data Acquisition & Capturing
- Data Processing & Analytics
- Modelling & Simulation
- Data Visualization
- User Interaction
- Digital Twin Development
|
3 |
0 |
| 2 |
IF5181 |
Information Technology Platform
- Fundamental concepts and key components of Information Technology platforms
- Business needs analysis and methods for selecting appropriate IT platforms
- System integration
- Security and scalability
|
3 |
0 |
| 3 |
IF5280 |
Digital Security, Privacy, and Forensics
- Reference models and principles of digital security
- Digital security vulnerabilities
- Digital threats and attacks
- Defensive and offensive digital security engineering
- Digital privacy requirements and regulations
- Implementation of digital privacy compliance
- Reactive digital forensics
- Proactive digital forensics
|
3 |
0 |
| 4 |
IF6180 |
Digital Transformation and Enterprise Architecture
- Basic Concepts and Relationship between Digital Transformation, Enterprise Architecture (EA), and Smart System
- Digital Transformation and It’s Frameworks
- Enterprise Architecture and It’s Frameworks
- Design Transformation Strategy and Plan
- Aligning Enterprise Architecture with Transformation Plan
- Design aligned Enterprise Architecture
- Case Studies
|
3 |
0 |