Automata Theory and Language field is the study of formal theory, formal languages, and automata. Students understand fundamental concepts in computing and modeling languages and systems used in informatics. First, students learn about formal languages, language structures, and grammar used to represent information and data processing. They understand how to write and recognize formal languages such as regular, context-free, context-sensitive, and related formal languages. Second, students understand automata, the underlying computational tools, and the capabilities and limitations of each. They study infinite automata, infinite automata, Turing machines, and languages acceptable to these automata. Next, students study complexity theory, which addresses the difficulty of computational problems, and also learn theoretical concepts such as NP-completeness and the analysis of time and space in algorithms. Finally, students practice the application of automata theory and language in software development, compilers, static analysis, and other related fields. They can design and develop efficient and precise systems according to the corresponding formal languages and automata.

Software Engineering in Informatics Engineering diploma program focuses on developing, designing, testing, and maintaining software. Students understand the latest concepts and techniques related to the software development life cycle, both for desktop, web, and mobile applications. First, students learn the principles of software engineering, including development methodologies such as the waterfall model, spiral model, and agile methods. They understand the stages from planning, requirement analysis, design, implementation, and testing, to software maintenance. Second, students explore programming languages and related technologies used in software development. They understand the concepts of algorithms, data structures, and system design, and are able to select and apply technologies that suit the needs of the project. Next, students understand the principles of user interface design (UI/UX) to ensure a good user experience. They also learn project management, software version management, and legal and ethical aspects of software development. Finally, students practice applying the theories and skills acquired in real projects. They learn to work in teams, communicate effectively, and complete projects according to the desired deadlines and quality standards.

Systems Programming in the Informatics Engineering diploma program focuses on developing the skills and knowledge needed to become experts in understanding, designing, and implementing low-level and high-level software systems. Students learn fundamental concepts of programming languages, algorithms, data structures, and performance optimization. First, students understand programming fundamentals such as syntax, semantics, and programming logic. They understand control structures, functions, and modularity in programming languages such as C, C++, Java, and Python. Secondly, students explore the concepts of data structures and efficient algorithms. They learn optimization techniques, algorithm complexity, and how to select the appropriate algorithm for problem-solving. Next, students understand the principles of operating system application development, including memory management, process management, file management, and input/output management. Finally, students practice the application of theories and skills in developing application systems and understand the latest technologies such as web-based application development, cloud computing, and the Internet of Things (IoT). They also learn the security aspects of software development.

Digital Systems in the Informatics Engineering diploma program examines concepts and techniques related to digital systems, focusing on the design, development, and analysis of digital-based systems. Students understand the basic principles of digital electronics, logic gates, and digital system structure. First, students learn about digital logic, including logic gates, logic functions, and Boolean algebra. They understand how to apply this logic in the design of digital circuits. Second, students learn about number systems and data representation in binary, octal, and hexadecimal forms. They also understand binary arithmetic and logical operations on binary data. Next, students delve into topics such as combinational circuits and sequence circuits, including circuit generation and analysis using Karnaugh maps, flip-flops, and registers. In addition, students learn about using simulation and design software such as Verilog or VHDL to design and model complex digital circuits. Finally, students are introduced to microprocessors, microcontrollers, and embedded systems. They understand the concepts of CPU architecture, assembly language, and programming principles at a low level.

Intelligent Systems in Informatics Engineering diploma program focuses on the study and development of systems that have the ability to understand, learn, and adapt to their environment. Intelligent systems integrate artificial intelligence, natural language processing, and machine learning algorithms to create intelligent and effective solutions. Students in this subfield study the fundamentals of artificial intelligence, including the underlying algorithms and data structures. They learn about machine learning methods, such as supervised, unsupervised, and reinforcement learning. Emphasis is placed on practical applications of artificial intelligence, such as recommendation systems, pattern recognition, and data analysis. In addition, students understand the principles of natural language processing, which enables communication between humans and computers using human language. They learn about sentiment analysis, language modeling, and other NLP applications. At an advanced level, students study topics such as artificial neural networks, fuzzy logic, and genetic algorithms used in the development of intelligent systems. They also delve into concepts about robotics and autonomous systems.

Computer Networking in Informatics Engineering diploma program focuses on the study and development of network infrastructure that enables communication and information exchange between electronic devices. Computer networks are the foundation of modern connectivity, playing an important role in providing access to the Internet, communication systems, and business applications. Students in this discipline learn basic networking concepts, communication protocols, network architecture, as well as network security principles. They learn about network topology, routing, switching, and network management. In addition, they explore the latest technologies such as wireless networks, sensor networks, and mobile networks. An understanding of network security including encryption, information security, and application security is also a key focus. Practicums involving the configuration and maintenance of network devices help students understand the application of theoretical concepts in a practical environment. Applied projects also provide opportunities for students to design, implement, and manage computer networks according to organizational or business needs.

Data Science and Analytics within the Informatics Engineering diploma program offers an in-depth understanding of data management, analysis, and interpretation to support intelligent decision-making. Students in this cohort learn about a range of data analysis techniques and the tools required to extract valuable insights from a variety of data sources. In the early stages, the focus is on understanding the basic concepts of statistics, math, and programming required to work with data. Students learn various analysis techniques such as regression, clustering, classification, and data visualization. They also understand the use of relevant software and programming languages such as Python, R, SQL, and modern data analysis tools. In addition, students understand data collection techniques, big data processing, and database management. The application of these methods in various contexts, including business, healthcare, finance, and science, is at the core of the curriculum. Practical projects help students apply the concepts they have learned in real-world situations. Communication and data interpretation skills are also emphasized, as data scientists need to be able to effectively articulate analysis results to stakeholders who may not have a technical background.

Numerical Analysis in Informatics Engineering diploma program discusses mathematical principles and techniques used to analyze and solve real problems with numerical approaches. The main focus is on developing efficient algorithms and computational methods to perform complex numerical calculations. Students in this group learn about numerical methods for solving differential equations, interpolation, numerical integration, optimization, solving nonlinear equations, and approximation methods. They also understand the importance of error analysis and algorithm stability in a numerical context. The use of programming languages such as Python, MATLAB, or other programming languages to implement algorithms and demonstrate the results of numerical analysis is an integral part of the curriculum. Students are given the opportunity to solve real problems using numerical algorithms, interpret the results, and make decisions based on numerical analysis. In addition, the group also places emphasis on the application of numerical analysis in specialized domains such as computer graphics, image processing, artificial intelligence, and finance. Students can develop an in-depth understanding of how numerical analysis is used to address challenges related to big data, portfolio optimization, simulation, and more.

Algorithm Design and Analysis in the Informatics Engineering diploma program addresses the principles, techniques, and methods underlying algorithm development and analysis. The main focus is on designing efficient and effective algorithms to solve various complex computational problems. Students in this group study various algorithmic paradigms such as greedy algorithms, divide and conquer, dynamic programming, backtracking, and heuristic algorithms. They learn to analyze algorithm performance in terms of time complexity, space complexity, and other factors that affect algorithm efficiency. The application of programming languages such as Python, Java, or C++ becomes an important part of the learning. Students will implement the algorithms they learn and understand how to optimize and adapt algorithms according to specific situations and needs. In addition, this group also focuses on understanding the data structures used in supporting various algorithms. Students learn about data structures such as arrays, linked lists, stacks, queues, trees, and graphs, and understand when and how to apply the right data structures to support the optimal performance of algorithms.