| Kode Mata Kuliah | IF5141 / 4 SKS |
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| Penyelenggara | 235 - Informatics / STEI |
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| Kategori | Lecture |
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| Bahasa Indonesia | English |
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| Nama Mata Kuliah | Penambangan Data | Data Mining |
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| Bahan Kajian | - Proses model untuk data mining (CRISP-DM)
- Konsep dasar data, statistik dan visualisasi dasar terkait data,
pengukuran (measurement), dan pemrosesan awal data (data pre-processing)
- Teknik dasar pattern mining terhadap frequent patterns,
associations, dan correlations
- Recall: Klasifikasi dan cluster analysis dengan teknik
pembelajaran mesin.
- Overview teknik-teknik pembelajaran mesin lanjut untuk
berbagai jenis data
- Evaluasi model data mining
- Deployment model data mining
- Studi kasus pembangunan model pembelajaran mesin untuk suatu persoalan/organisasi: pemahaman bisnis, pemahaman data, persiapan data, pembangunan model, evaluasi model, deployment
| - Data mining methodology (CRISP-DM)
- Basic concepts of data, statistics and basic visualization related to data, measurement and data pre-processing
- Basic pattern mining techniques for frequent patterns, associations, and correlations
- Recall: Classification and cluster analysis with machine learning techniques.
- Overview of advanced machine learning techniques for various types of data
- Evaluation of data mining models
- Deployment of data mining models
- Case study of building a machine learning model for a problem/organization: business understanding, data understanding, data preparation, model building, model evaluation, deployment
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| Capaian Pembelajaran Mata Kuliah (CPMK) | - Mampu memformulasikan kebutuhan bisnis terkait analisis data dan menjelaskan pemahaman terhadap data organisasi yang digunakan untuk analisis data.
- Mampu mempersiapkan data berdasarkan karakteristik data dalam rangka membangun model pembelajaran mesin yang sesuai.
- Mampu membangun solusi model pembelajaran mesin yang sesuai dengan persoalan bisnis dan melakukan deployment sebagai bagian dari solusi persoalan bisnis.
- Mampu mengevaluasi solusi data mining dan deployment-nya untuk mengukur ketercapaian kebutuhan bisnis.
| - Formulate business needs related to data analysis and explain understanding of organizational data used for data analysis
- Prepare data based on data characteristics in order to build appropriate machine learning models.
- build machine learning model solutions that suit business problems and deploy them as part of the solution to business problems.
- evaluate data mining solutions and their deployment to measure the achievement of business needs.
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| Metode Pembelajaran | -Ceramah, diskusi, dan tanya jawab, dikombinasi dengan flipped classroom
-Case-based learning melalui praktikum
-Project-based learning
-Presentasi mahasiswa dan tanya jawab | Lecture, discussion, question & answering, combined by flipped classroom
Case-based learning by labworks
Project-based learning
Student presentation |
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| Modalitas Pembelajaran | Hybrid
Visual and auditorial | Hybrid
Visual and auditorial |
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| Jenis Nilai | ABCDE |
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| Metode Penilaian | Ujian/kuis tertulis
PR/Tugas/Praktikum
Tugas Besar | Examination, Quiz, Practical test, Assignment |
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| Catatan Tambahan | | |
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