Mata Kuliah BI4118 - 2024

Kode Mata KuliahBI4118 / 2 SKS
Penyelenggara106 - Biology / SITH
KategoriLecture
Bahasa IndonesiaEnglish
Nama Mata KuliahPengantar Analisis Big Data HayatiIntroduction to Biological Big Data Analysis
Bahan Kajian
  1. Konsep Data, Big Data, dan Kecerdasan Buatan dalam Ilmu Hayati
  2. Machine Learning dan Data Mining untuk Ilmu Hayati
  3. Arsitektur Data, Model Data, dan Etika Big Data
  4. Biologi Sistem dan Teknik Analisis Big Data
  5. Aplikasi Big Data Analytics dalam Ilmu Hayati
  1. Data, Big Data, and Artificial Intelligence in Life Sciences
  2. Machine Learning and Data Mining for Life Sciences
  3. Data Architecture, Data Models, and Big Data Ethics
  4. Systems Biology and Big Data Analytics Techniques
  5. Applications of Big Data Analytics in Life Sciences
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Mahasiswa mampu menjelaskan konsep Big Data serta perkembangan dan penerapannya dalam berbagai bidang ilmu hayati.
  2. Mahasiswa mampu menggunakan teknik-teknik Data Mining yang sesuai untuk melakukan analisis data hayati berskala besar.
  3. Mahasiswa mampu menerapkan pengetahuan dan keterampilan yang diperoleh untuk melakukan analisis dasar Big Data, menyajikan hasil analisis, serta menginterpretasikan hasilnya dalam konteks ilmu hayati.
  1. Students are able to explain the concepts of Big Data and its development and applications in various fields of life sciences.
  2. Students are able to apply appropriate Data Mining techniques to analyze large-scale biological data.
  3. Students are able to apply acquired knowledge and skills to perform basic Big Data analyses, present analytical results, and interpret the findings in the context of life sciences.
Metode PembelajaranBersifat terstruktur dan bauran (blended learning). Pembelajaran dilakukan melalui kuliah interaktif, tutorial, studi kasus, diskusi kelompok, presentasi, serta latihan analisis data menggunakan perangkat lunak yang relevan.Structured and blended learning. Learning activities are conducted through interactive lectures, tutorials, case studies, group discussions, presentations, and data analysis exercises using relevant software tools.
Modalitas PembelajaranMenggabungkan berbagai moda penyerapan: Visual melalui diagram, dataset, visualisasi data, dan demonstrasi perangkat lunak. Auditorial melalui kuliah, diskusi, presentasi, dan tanya jawab. Kinestetik melalui latihan analisis data, eksplorasi dataset, dan penyelesaian studi kasus. Sinkron dan asinkron melalui kombinasi pembelajaran tatap muka dan platform digital.Combining various learning styles: Visual, through diagrams, datasets, data visualizations, and software demonstrations. Auditory, through lectures, discussions, presentations, and question-and-answer sessions. Kinesthetic, through data analysis exercises, dataset exploration, and case study activities. Synchronous and asynchronous, through a combination of face-to-face and digital learning platforms.
Jenis NilaiABCDE
Metode PenilaianDilakukan secara terpadu untuk menilai: Aspek pengetahuan melalui kuis, UTS, dan UAS. Aspek keterampilan melalui tugas analisis data, studi kasus, dan presentasi. Aspek sikap melalui partisipasi aktif, integritas akademik, dan kemampuan bekerja sama.Conducted in an integrated manner to assess: Knowledge through quizzes, midterm examinations, and final examinations. Skills through data analysis assignments, case studies, and presentations. Attitudes through active participation, academic integrity, and teamwork.
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