Kode Mata KuliahIF5141 / 4 SKS
Penyelenggara235 - Informatika / STEI
KategoriKuliah
Bahasa IndonesiaEnglish
Nama Mata KuliahPenambangan DataData Mining
Bahan Kajian
  1. Proses model untuk data mining (CRISP-DM)
  2. Konsep dasar data, statistik dan visualisasi dasar terkait data, pengukuran (measurement), dan pemrosesan awal data (data pre-processing)
  3. Teknik dasar pattern mining terhadap frequent patterns, associations, dan correlations
  4. Recall: Klasifikasi dan cluster analysis dengan teknik pembelajaran mesin.
  5. Overview teknik-teknik pembelajaran mesin lanjut untuk berbagai jenis data
  6. Evaluasi model data mining
  7. Deployment model data mining
  8. Studi kasus pembangunan model pembelajaran mesin untuk suatu persoalan/organisasi: pemahaman bisnis, pemahaman data, persiapan data, pembangunan model, evaluasi model, deployment
  1. Data mining methodology (CRISP-DM)
  2. Basic concepts of data, statistics and basic visualization related to data, measurement and data pre-processing
  3. Basic pattern mining techniques for frequent patterns, associations, and correlations
  4. Recall: Classification and cluster analysis with machine learning techniques.
  5. Overview of advanced machine learning techniques for various types of data
  6. Evaluation of data mining models
  7. Deployment of data mining models
  8. Case study of building a machine learning model for a problem/organization: business understanding, data understanding, data preparation, model building, model evaluation, deployment
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Mampu memformulasikan kebutuhan bisnis terkait analisis data dan menjelaskan pemahaman terhadap data organisasi yang digunakan untuk analisis data.
  2. Mampu mempersiapkan data berdasarkan karakteristik data dalam rangka membangun model pembelajaran mesin yang sesuai.
  3. Mampu membangun solusi model pembelajaran mesin yang sesuai dengan persoalan bisnis dan melakukan deployment sebagai bagian dari solusi persoalan bisnis.
  4. Mampu mengevaluasi solusi data mining dan deployment-nya untuk mengukur ketercapaian kebutuhan bisnis.
  1. Formulate business needs related to data analysis and explain understanding of organizational data used for data analysis
  2. Prepare data based on data characteristics in order to build appropriate machine learning models.
  3. build machine learning model solutions that suit business problems and deploy them as part of the solution to business problems.
  4. evaluate data mining solutions and their deployment to measure the achievement of business needs.
Metode Pembelajaran-Ceramah, diskusi, dan tanya jawab, dikombinasi dengan flipped classroom -Case-based learning melalui praktikum -Project-based learning -Presentasi mahasiswa dan tanya jawabLecture, discussion, question & answering, combined by flipped classroom Case-based learning by labworks Project-based learning Student presentation
Modalitas PembelajaranHybrid Visual and auditorialHybrid Visual and auditorial
Jenis NilaiABCDE
Metode PenilaianUjian/kuis tertulis PR/Tugas/Praktikum Tugas BesarExamination, Quiz, Practical test, Assignment
Catatan Tambahan