Kurikulum 2024

Kode Mata KuliahMB6007 / 3 SKS
Penyelenggara290 - Management Science / SBM
KategoriLecture
Bahasa IndonesiaEnglish
Nama Mata KuliahAnalisis Deret Waktu dan Analitika DataTime Series Analysis and Data Analytics
Bahan Kajian
  1. Pengantar Time Series dan Data Analytics: Pengenalan konsep time series dan pentingnya analisis data dalam bisnis
  2. Komponen Time Series: Trend, musiman, siklus, dan variasi acak
  3. Teknik Decomposisi Time Series: Decomposisi aditif dan multiplikatif
  4. Smoothing Techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing
  5. ARIMA Models: Autoregressive, Integrated, and Moving Average (ARIMA)
  6. Pemodelan Musiman (SARIMA): Seasonal ARIMA Models
  7. Data Analytics untuk Time Series: Penggunaan analitik data untuk eksplorasi time series
  8. Data Preparation: Persiapan data untuk analitik time series, pengolahan data hilang, outliers
  9. Forecasting dan Predictive Analytics: Teknik-teknik prediksi dengan data time series
  10. Machine Learning untuk Time Series: Penggunaan metode machine learning (misalnya Random Forest, Neural Networks) untuk prediksi data time series
  11. Model Evaluasi: Validasi model prediksi, cross-validation untuk time series
  12. Visualisasi Data Time Series: Teknik visualisasi untuk presentasi hasil analisis data
  1. Introduction to Time Series and Data Analytics: Introduction to the concept of time series and the importance of data analysis in business
  2. Components of Time Series: Trend, seasonal, cyclical, and irregular variations
  3. Time Series Decomposition Techniques: Additive and multiplicative decomposition
  4. Smoothing Techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing
  5. ARIMA Models: Autoregressive, Integrated, and Moving Average (ARIMA)
  6. Seasonal Modeling (SARIMA): Seasonal ARIMA Models
  7. Data Analytics for Time Series: Using data analytics to explore time series data
  8. Data Preparation: Preparing data for time series analytics, handling missing data, and outliers
  9. Forecasting and Predictive Analytics: Prediction techniques with time series data
  10. Machine Learning for Time Series: Applying machine learning methods (e.g., Random Forest, Neural Networks) for time series prediction
  11. Model Evaluation: Validation of predictive models, cross-validation for time series
  12. Time Series Data Visualization: Visualization techniques to present time series analysis results
Capaian Pembelajaran Mata Kuliah (CPMK)
  1. Mahasiswa mampu mengidentifikasi dan menganalisis komponen time series seperti tren, musiman, siklus, dan variasi acak dari berbagai dataset.
  2. Mahasiswa mampu menerapkan metode pemodelan deret waktu seperti ARIMA dan SARIMA untuk membuat prediksi berdasarkan data historis.
  3. Mahasiswa mampu mempersiapkan, membersihkan, dan mengolah dataset time series, termasuk penanganan data hilang dan outliers.
  4. Mahasiswa mampu menerapkan algoritma machine learning seperti Random Forest dan Neural Networks untuk memprediksi data time series secara akurat.
  5. Mahasiswa mampu memvisualisasikan hasil analisis time series secara efektif dan menyajikan informasi yang relevan untuk pengambilan keputusan bisnis.
  1. Students are able to identify and analyze time series components such as trends, seasonality, cycles, and irregular variations from various datasets.
  2. Students are able to apply time series modeling methods such as ARIMA and SARIMA to make predictions based on historical data.
  3. Students are able to prepare, clean, and process time series datasets, including handling missing data and outliers.
  4. Students are able to apply machine learning algorithms, such as Random Forest and Neural Networks, to accurately predict time series data.
  5. Students are able to effectively visualize time series analysis results and present relevant information for business decision-making.
Metode PembelajaranPerkuliahan, simulasi, diskusi kelas, studi kasusLectures, simulations, class discussions, case study
Modalitas PembelajaranLuring, sinkron, mandiriOffline, synchronous, individual
Jenis NilaiABCDE
Metode PenilaianUjian akhir semester, proyek individual, tugas (individu dan kelompok)Final test, individual project, assignment (individual and group)
Catatan Tambahan