MATA KULIAH PILIHAN S1 INFORMATIKA KURIKULUM 2020

Berikut adalah daftar MK Pilihan yang ditawarkan pada Kurikulum 2020 Prodi S1 Informatika.

Khusus untuk semester Ganjil 20/21 hanya di buka MK Pilihan Peminatan 2 dan MK Pilihan Bebas

Detail penjelasan tentang apa itu mk peminatan dan mk pilihan bebas akan dijelaskan saat sosialisasi kurikulum 2020 (coming soon)

MK PILIHAN PEMINATAN 1

KODENAMA MATA KULIAHELECTIVE COURSE NAMEDescriptionRPS
CII-3L3Pembelajaran Mesin LanjutAdvanced Machine LearningAdvanced Machine Learning - The course of Advanced Machine Learning (AML) trains students to understand basic notions, intuitions, concepts, techniques, and algorithms to develop a machine learning model based on the given data sets. The material includes evolutionary computation (EC), swarm intelligence (SI), evolutionary shallow learning (ESL), evolutionary deep learning (EDL), evolutionary ensemble learning (EEL), and evolutionary reinforcement learning (ERL). RPS Pembelajaran Mesin Lanjut
CII-3M3Representasi PengetahuanKnowledge RepresentationThis course is to provide an introduction to knowledge representation and reasoning, which is one of the fundamental areas in artificial intelligence.The course begins with a review of first order logic and resoning/inference. The main concepts students will learn in this course are description logic and its representation in ontology in OWL. Ontology evaluation and query are also introduced to give students knowledge about processes to have valid ontologies and information retrieval from ontologies. Finally, logic and probability will be introduced to give students a view how the concepts of knowledge representation and reasoning can be combined with the concepts of probability in intelligent systems. RPS Respresentasi Pengetahuan
CII-3N3Sistem Multi AgenMulti Agent SystemIn this course students will be introduced to the concept of intelligent agents and the main issues surrounding the design of intelligent agents. Students will also discuss the key issues in designing multiagent society that involves communication, cooperation, and strategies for decision making. RPS Sistem Multi Agen
CII-3O3IoT dengan Kemampuan CerdasAI-enabled IoTThis course elaborates the application of intelligent system for conceptual design and implementation in IoT area, including classification, regression and interpolation, Fuzzy logic method for controller with IoT based, Application of intelligent system for smart building, growth network, sensor network, and environmental control. Students are expected to active in the class and outside class (laboratorium) using team works base (2-3 people). The course is in 7 times meeting for IoT project such as literature study, design and implementation of project. RPS IoT dengan Kemampuan Cerdas
CII-3P3Pemodelan dan SimulasiModelling and SimulationModeling and Simulation course provides the knowledge and basic skill to be able to create a model and make a simulation of daily life phenomena. Generally, the course material consists of two types of modeling, i.e. deterministic and stochastic modeling. In this course, several algorithms of artificial intelligence are implemented to simulate some simple phenomena.RPS Pemodelan dan Simulasi

MK PILIHAN PEMINATAN 2

KODENAMA MATA KULIAHELECTIVE COURSE NAMEDescriptionRPS
CII-4F3Pemrosesan Citra DigitalDigital Image ProcessingThis course provide the understanding of image representation, basic image operations, image enhancement, convolution and fourier transformation processes, image segmentation, image morphology processes, image compression and Fidelity Criteria for various operations on digital images.RPS Pemrosesan Citra Digital
CII-4G3Pemrosesan Bahasa AlamiNatural Language ProcessingIn this course, students will learn about language processing on lexical, syntactic, and semantic levels. The approach discussed focuses on machine learning based methods, including deep learning. At the end of the lecture, the popular NLP applications will be discussed as the implementation of material studied before.
RPS Pemrosesan Bahasa Alami
CII-4H3Sistem Pemberi RekomendasiRecommender systemThis course studies the recommendation system's methods, which are based on: collaborative, content, context, knowledge and hybrids. Besides students will learn how to recommend a item to the group, explain the recommendations to users, as well as deep learning applications in the recommendation system. RPS Sistem Pemberi Rekomendasi
CII-4I3Penambangan DataData MiningIn this course students learn the definitions of Data Mining, the Background of Data Mining and the benefits of Data Mining in supporting decision making in business. Good decision making must be based on information supported by data held by the organization, both from within the organization itself and data from outside the organization. To produce this information various techniques such as classification, clustering and association analysis will be used.
RPS Penambangan Data
CII-4J3Analisis Performansi Jaringan KomputerAnalysis of Computer Network PerformanceThis lecture discusses the modeling and analysis of computer networks performance. The lecture materials include network protocols, and applications. At the end of this lecture, students are expected to be able to use mathematical modeling of computer networks to analyze network performances. The topics discussed in this lecture include the characteristics of IP traffic and network performance monitoring, review of congestion control, fairness and scheduling, and queuing systems to model performance.RPS Analisis Performansi Jaringan Komputer
CII-4K3Sistem Keamanan CerdasIntelligent Security SystemThis unit aims at providing students conceptual and practical aspects of how artificial intelligence (AI) can be used in the context of system security in both offense and defense. The unit covers fundamentals of system security, network specific threats and attack types, architectures for secure networks, examples of malware, defense mechanisms and countermeasures, fundamentals of AI for system security, AI-based approaches for defending against attacks, and seven use cases of the implementation of AI in system security: malware identification, threat detection, proactive response, autonomous patch deployment, adapt to changing threats, spam and phishing detection, and categorise attacks. RPS Sistem Keamanan Cerdas
CII-4L3Visualisasi DataData VisualizationThis course is all about data visualization, the art and several techniques of turning data into readable graphics presentation. Students learn how to design and implement the visualization based on the available data and the goal to be achieved. This course points the use of visualization for data analytics supporting several topics in machine learning. Students will design and create their own data visualization and learn how to use a tools such a Gnuplot, Matplotlib, and interactive data visualization with Python's Bokeh.RPS Visualisasi Data
CII-4M3Metode Numerik untuk InformatikaNumerical Method for InformaticsThis course elaborates some numerical methods for solving several problems in application of computer science or informatics area. Moreover, this course will be focused on numerical approach for tackling the Artificial Intelligent problems such as classification and image processing. RPS Metode Numerik untuk Informatika

MK PILIHAN BEBAS

KODENAMA MATA KULIAHELECTIVE COURSE NAMEDescriptionRPS
CII-4N3Desain InteraksiInteraction DesignThis course will provide an overview of the basic concepts used in Interaction Design that originate from research. Students will be given knowledge of how a designer thinks in answering a problem. Then proceed with the stages in making an interaction design solution that starts from user research and defining the problem, understanding the user, making the design of the solution to become a prototype that will be evaluated by testing usability and UX. This course can provide students with sufficient knowledge in supporting intelligent system-based software by considering user convenience for best user experience.

RPS Desain Interaksi
CII-4O3Analisis Jejaring SosialSocial Network AnalysisIn this course students learn about: (1) the definition and fundamental models of Social Network Analysis; (2) network types, structures, models, and dynamic processes on social networks; (3) calculation methods of the social networks centrality; (4) methods for identifying communities in social networks; (5) software for implementing social network analysis; (6) visualization of social networks.
This course uses Twitter social network case studies.

RPS Analisis Jejaring Sosial
CII-4P3Komputasi Berkinerja TinggiHigh Performance ComputingHigh performance computing subject will provide lecture on latest technology in the area of computing, it will expand the students knowledge to learn the cutting-edge technology in the scientific and artificial intelligence area.
Students will be encouraged to learn and experience the parallel computing technique in the real supercomputer, starting from the basic concept of SIMD (Single Instruction Multiple Data) and MIMD (Multi Instruction Multiple Data) until the programming implementation in the real computing challenge case. Students will be able to learn CUDA and MPI programming as the low level programming language, also they will be stimulated to use the high level language framework based on python such as Tensorflow, PyTorch, or Caffe that executed at the high performance hardware.
We expect by mastering this abilities, students can tackle future challenging problem in their own fields.
RPS Komputasi Berkinerja Tinggi
CII-4Q3Visi KomputerComputer VisionThis course provides a foundation for the concept of building recognition system that tries to imitate the human ability in recognizing visual object by using classical methods and state-of-the-art methods. Many machine learning and deep learning methods in building recognition system are introduced to students so students are able to design, implement and measure the performance of a recognition system. RPS Visi Komputer
CII-4R3Forensik DigitalDigital ForensicsThe course provides insight into the scope of the digital forensic field which generally consists of two parts, namely mobile phone forensics and multimedia forensics. Mobile phone forensics focuses on the latest forensic techniques in the investigation of mobile devices across various mobile platforms, especially on iOS, Android and Windows 10. We will learn on retrieving data from a mobile phone under forensically sound conditions. Whereas multimedia forensics studies a set of scientific techniques for analyzing multimedia signals (audio, video, images) in order to recover probative evidences to reveal the history of digital content which includes identification of acquisition devices that produced the data, and validation of content integrity.RPS Forensik Digital
CII-4S3Verifikasi dan Validasi Perangkat LunakSoftware Verification and ValidationSoftware Verification and Validation is an elective course in a series of software engineering courses in the undergraduate of computer science program. This course describes the software verification and validation concepts and its implementation, to assure the quality of software. In this course, student will learn about quality concepts and quality models, distinguish between program validation and verification, the different types and levels of software testing , and tools that can be used in the validation of software, create and document a set of tests for a medium-size code segment , describing how to select good regression tests and automate them, and use a defect tracking tool to manage software defects in a small software project.RPS Verifikasi dan Validasi PL