Mata Kuliah ET4244 - 2024

Kode Mata KuliahET4244 / 3 SKS
Penyelenggara181 - Telecommunication Engineering / STEI
KategoriLecture
Bahasa IndonesiaEnglish
Nama Mata KuliahOptimisasi untuk TelekomunikasiTelecommunication Optimization
Bahan Kajian
  1. Fungsi Aljabar
  2. Sistem Persamaan Linier
  3. Desain Jaringan
  4. Konsep Nirkabel
  5. Optimisasi
  1. Algebraic Functions
  2. Systems of Linear Equations
  3. Network Design
  4. Wireless Concept
  5. Optimization
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Kemampuan mengidentifikasi dan merumuskan permasalahan teknik terkait fungsi aljabar, sistem persamaan linier, desain jaringan, dan konsep nirkabel.
  2. Kemampuan menganalisis dan menyelesaikan permasalahan teknik terkait fungsi aljabar, sistem persamaan linier, desain jaringan, dan konsep nirkabel.
  3. Kemampuan merencanakan serta menyelesaikan tugas secara sistematis sesuai dengan spesifikasi yang telah ditetapkan dalam konteks fungsi aljabar, sistem persamaan linier, desain jaringan, dan konsep nirkabel.
  4. Kemampuan mengevaluasi tugas dalam batasan yang ada terkait fungsi aljabar, sistem persamaan linier, desain jaringan, dan konsep nirkabel.
  1. Ability to identify and formulate engineering problems related to algebraic functions, systems of linear equations, network design, and wireless concepts.
  2. Ability to analyze and solve engineering problems related to algebraic functions, systems of linear equations, network design, and wireless concepts.
  3. Ability to plan and complete tasks systematically according to specifications that have been set in the context of algebraic functions, systems of linear equations, network design, and wireless concepts.
  4. Ability to evaluate assignments within existing constraints regarding algebraic functions, systems of linear equations, network design, and wireless concepts.
Metode PembelajaranCeramahLecture
Modalitas PembelajaranLuring Sinkron Daring Asinkron BauranSynchronous Offline Asynchronous Online Mix
Jenis NilaiABCDE
Metode PenilaianKuis, UTS, UASQuizzes, Mid Semester Exam, Final Exam
Catatan TambahanDeskripsi kuliah: Mata kuliah ini memberikan pemahaman dasar mengenai konsep dan prinsip optimisasi bagi pemula. Pentingnya optimisasi dalam kehidupan sehari-hari dijelaskan melalui berbagai aplikasi nyata, seperti sistem rekomendasi Netflix, penentuan harga layanan internet, pengendalian daya (power control), lokalisasi GPS, beamforming, serta user association dalam jaringan komunikasi. Di era kecerdasan buatan, optimisasi merupakan salah satu pilar utama dalam implementasi machine learning. Mata kuliah ini juga membahas konsep learning to optimize khususnya pada kasus pengendalian daya, serta menjelaskan perbedaan antara dua varian utama deep learning, yaitu unsupervised learning dan deep reinforcement learning. Topik yang dibahas: 1. Pengantar optimisasi dan aplikasinya, 2. Dasar-dasar optimisasi konveks dan non-konveks, 3. Algoritma iteratif untuk optimisasi constraintless, 4. Optimisasi dengan constraints, 5. Metode Interior Point (IPM), 6. Metode Penalti dan Augmented Lagrangian Method (ALM), 7. Successive Convex Approximation (SCA), 8. Block Coordinate Descent (BCD) dan Parallel SCA, 9. Optimisasi global, 10. Learning to Optimize, 11, Optimisasi metaheuristikCourse description: This course provides a basic understanding of the concept and principles of optimization for beginners. The importance of optimization in daily life is explained through various real applications such as the Netflix recommendation system, internet pricing, power control, GPS localization, beamforming, and user association. In the era of artificial intelligence, optimization is one of the main pillars in the implementation of machine learning. This course also discusses the concept of learning to optimize for power control cases and the differences between two variants of deep learning: unsupervised learning and deep reinforcement learning. Topics of discussion include: 1. Introduction to optimization and its application, 2. Basis of convex and non-convex optimization, 3. Iterative algorithms for constraintless optimization, 4. Optimization with constraints, 5. Interior Point Method (IPM), 6. Penalty Method and Augmented Lagrangian Method (ALM), 7. Successive Convex Approximation (SCA), 8. Block Coordinate Descent (BCD) and Parallel SCA, 9. Global optimization, 10. Learning to Optimize, 11. Metaheuristic optimization