Kode Mata KuliahMS5100 / 4 SKS
Penyelenggara231 - Mechanical Engineering / FTMD
KategoriLecture
Bahasa IndonesiaEnglish
Nama Mata KuliahPerancangan Eksperimen dan Analitika DataDesign of Experiment and Data Analytics
Bahan Kajian
  1. Analisis Data Eksplorasi
  2. Analisis Varians
  3. Blocking
  4. Desain Faktorial
  5. Eksperimen Komputer
  6. Metodologi Permukaan Respons
  7. Pembelajaran Mesin: Metodologi Permukaan Respons & Regresi
  8. Pembelajaran Mesin: Klasifikasi
  9. Pembelajaran Mesin: Pembelajaran Tanpa Pengawasan
  1. Exploratory Data Analysis
  2. Analysis of Variance (ANOVA)
  3. Blocking
  4. Factorial Design
  5. Computer Experiment
  6. Response Surface Methodologies
  7. Machine Learning: Response Surface Methodologies & Regression
  8. Machine Learning: Classification
  9. Machine Learning: Unsupervised Learning
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Memahami prinsip dasar dan mampu melakukan exploratory data analysis untuk cek karakteristik data
  2. Memahami prinsip-prinsip penting perancangan eksperimen, mencakup ANOVA, Blocking, dan Factorial Design, dan penerapannya.
  3. Memahami prinsip dasar dan teknik-teknik dasar computer experiment
  4. Memahami prinsip machine learning: response surface methodologies dan metode regresi, serta mampu membuat model regresi berdasarkan data
  5. Memahami prinsip machine learning: metode klasifikasi dan unsupervised learning, serta mampu membuat model klasifikasi dan unsupervised learning berdasarkan data
  1. Understanding the basic principles and being able to perform exploratory data analysis to check the characteristics of the data
  2. Understanding the key principles of experimental design, including ANOVA, Blocking, and Factorial Design, and their application
  3. Understanding the basic principles and techniques of computer experiments
  4. Understanding the principles of machine learning: response surface methodologies and regression methods, and being able to create regression models based on data
  5. Understanding the principles of machine learning: classification and unsupervised learning methods, and being able to create classification and unsupervised learning models based on data
Metode PembelajaranTatap muka di kelas. Praktikum menggunakan Python/R.In-person class sessions. Laboratory activities using Python/R.
Modalitas PembelajaranLuring, sinkron, mandiri dan kelompok.Offline, synchronous, independent, and group
Jenis NilaiABCDE
Metode PenilaianUjian Tengah Semester. Ujian Akhir Semester. Tugas.Midterm Examination. Final Examination. Assignments.
Catatan Tambahan