| Kode Mata Kuliah | MS5100 / 4 SKS |
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| Penyelenggara | 231 - Teknik Mesin / FTMD |
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| Kategori | Kuliah |
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| Bahasa Indonesia | English |
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| Nama Mata Kuliah | Perancangan Eksperimen dan Analitika Data | Design of Experiment and Data Analytics |
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| Bahan Kajian | - Analisis Data Eksplorasi
- Analisis Varians
- Blocking
- Desain Faktorial
- Eksperimen Komputer
- Metodologi Permukaan Respons
- Pembelajaran Mesin: Metodologi Permukaan Respons & Regresi
- Pembelajaran Mesin: Klasifikasi
- Pembelajaran Mesin: Pembelajaran Tanpa Pengawasan
| - Exploratory Data Analysis
- Analysis of Variance (ANOVA)
- Blocking
- Factorial Design
- Computer Experiment
- Response Surface Methodologies
- Machine Learning: Response Surface Methodologies & Regression
- Machine Learning: Classification
- Machine Learning: Unsupervised Learning
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| Capaian Pembelajaran Mata Kuliah (CPMK) | - Memahami prinsip dasar dan mampu melakukan exploratory data analysis untuk cek karakteristik data
- Memahami prinsip-prinsip penting perancangan eksperimen, mencakup ANOVA, Blocking, dan Factorial Design, dan penerapannya.
- Memahami prinsip dasar dan teknik-teknik dasar computer experiment
- Memahami prinsip machine learning: response surface methodologies dan metode regresi, serta mampu membuat model regresi berdasarkan data
- Memahami prinsip machine learning: metode klasifikasi dan unsupervised learning, serta mampu membuat model klasifikasi dan unsupervised learning berdasarkan data
| - Understanding the basic principles and being able to perform exploratory data analysis to check the characteristics of the data
- Understanding the key principles of experimental design, including ANOVA, Blocking, and Factorial Design, and their application
- Understanding the basic principles and techniques of computer experiments
- Understanding the principles of machine learning: response surface methodologies and regression methods, and being able to create regression models based on data
- Understanding the principles of machine learning: classification and unsupervised learning methods, and being able to create classification and unsupervised learning models based on data
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| Metode Pembelajaran | Tatap muka di kelas.
Praktikum menggunakan Python/R. | In-person class sessions.
Laboratory activities using Python/R. |
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| Modalitas Pembelajaran | Luring, sinkron, mandiri dan kelompok. | Offline, synchronous, independent, and group |
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| Jenis Nilai | ABCDE |
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| Metode Penilaian | Ujian Tengah Semester.
Ujian Akhir Semester.
Tugas. | Midterm Examination.
Final Examination.
Assignments. |
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| Catatan Tambahan | | |
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