Kurikulum 2024

Kode Mata KuliahSK5009 / 4 SKS
Penyelenggara209 - Sains Komputasi / FMIPA
KategoriKuliah
Bahasa IndonesiaEnglish
Nama Mata KuliahKecerdasan Buatan LanjutAdvanced Artificial Intelligence
Bahan Kajian
  1. Pendahuluan: Sistem Kecerdasan Buatan (Artificial intelligent), Intelligent agents
  2. Teori Dasar Pertimbangan Probabilistik (Probabilistic Reasoning)
  3. Logika Samar (Fuzzy Logic) 1: Definisi logika samar, mengenal perbedaan himpunan klasik (Classical Sets) dan logika samar (Fuzzy Sets), Konsep relasi antara logika klasik dan logika samar (Classical and Fuzzy Relations)
  4. Logika Samar (Fuzzy Logic) 2: Membership function dan Defuzzification, Fuzzy Rule-Based System
  5. Logika Samar (Fuzzy Logic) 3: Fuzzy Decision Making, Applications of Fuzzy Logic
  6. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 1: Prinsip kerja.
  7. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 2: Ppendahuluan dan Rosenblatt’s Perceptron
  8. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 3: Model Building through Regression, The Least-Mean-Square Algorithm
  9. aringan Sel-syaraf Tiruan (Artificial Neural Networks) 4: Multilayer Perceptrons
  10. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 5: Radial-Basis Function Networks
  11. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 6: Support Vector Machines
  12. Jaringan Sel-syaraf Tiruan (Artificial Neural Networks) 7: Self-Organizing Maps
  13. Pembelajaran Berbasis Riset 1: Presentasi proposal proyek RBL
  14. Pembelajaran Berbasis Riset 2: Diskusi bimbingan proyek RBL di kelas atau Lab komputer, presentasi laporan kemajuan
  15. Pembelajaran Berbasis Riset 3: Diskusi bimbingan proyek RBL di kelas atau Lab komputer, presentasi laporan kemajuan
  16. Pembelajaran Berbasis Riset 4: Diskusi bimbingan proyek RBL di kelas atau Lab komputer, presentasi laporan kemajuan, presentasi akhir
  1. Introduction: Artificial Intelligence Systems, Intelligent agents
  2. Basic Theory of Probabilistic Reasoning
  3. Fuzzy Logic 1: Definition of fuzzy logic, getting to know the difference between classical sets (Classical Sets) and fuzzy logic (Fuzzy Sets), Concept of the relationship between classical logic and fuzzy logic (Classical and Fuzzy Relations)
  4. Fuzzy Logic 2: Membership function and Defuzzification, Fuzzy Rule-Based System
  5. Fuzzy Logic 3: Fuzzy Decision Making, Applications of Fuzzy Logic
  6. Artificial Neural Networks (Artificial Neural Networks) 1: Working principles.
  7. Artificial Neural Networks 2: Introduction and Rosenblatt's Perceptron
  8. Artificial Neural Networks 3: Model Building through Regression, The Least-Mean-Square Algorithm
  9. Artificial Neural Networks 4: Multilayer Perceptrons
  10. Artificial Neural Networks 5: Radial-Basis Function Networks
  11. Artificial Neural Networks 6: Support Vector Machines
  12. Artificial Neural Networks 7: Self-Organizing Maps
  13. Research Based Learning 1: RBL project proposal presentation
  14. Research Based Learning 2: RBL project guidance discussion in class or computer lab, progress report presentation
  15. Research Based Learning 3: RBL project guidance discussion in class or computer lab, progress report presentation
  16. Research Based Learning 4: RBL project guidance discussion in class or computer lab, progress report presentation, final presentation
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Mahasiswa mampu memahami bagaimana ide dasar sistem kecerdasan buatan dirancang.
  2. Mahasiswa mampu memahami metode apa saja yang telah dikembangkan di sistem kecerdasan buatan dan diaplikasikan untuk apa selama ini.
  3. Mahasiswa mampu menguasai setidaknya dua metode dalam sistem kecerdasan buatan, yaitu persoalan klasifikasi menggunakan logika fuzzy, dan metode pembelajaran menggunakan artificial neural network.
  4. Mahasiswa mampu membuat program komputer dan memahaminya untuk dapat diaplikasikan ke sistem.
  1. Students are able to understand how the basic ideas of artificial intelligence systems are designed.
  2. Students are able to understand what methods have been developed in artificial intelligence systems and what they have been applied for so far.
  3. Students are able to master at least two methods in artificial intelligence systems, namely classification problems using fuzzy logic, and learning methods using artificial neural networks.
  4. Students are able to create computer programs and understand them so they can be applied to systems.
Metode PembelajaranCeramah, diskusi, pembelajaran berbasis riset/masalah/studi kasus, studi literatur, kerja kelompok/mandiri,presentasi, praktekLectures, discussions, research/problem/case study based learning, literature studies, group/independent work, presentations, practice
Modalitas PembelajaranLuring/daring/hybrid, sinkronous dan asinkronousOffline/online/hybrid, synchronous and asynchronous
Jenis NilaiABCDE
Metode PenilaianPenilaian diberikan melalui PR / Tugas / Kuis / Praktikum / RBL / Laporan / Presentasi / UTS / UASAssessment is given through Homework / Assignments / Quizzes / Practicum / RBL / Reports / Presentations / UTS / UAS
Catatan Tambahan