Kode Mata Kuliah | MB6007 / 3 SKS |
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Penyelenggara | 290 - Sains Manajemen / SBM |
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Kategori | Kuliah |
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| Bahasa Indonesia | English |
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Nama Mata Kuliah | Analisis Deret Waktu dan Analitika Data | Time Series Analysis and Data Analytics |
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Bahan Kajian | - Pengantar Time Series dan Data Analytics: Pengenalan konsep time series dan pentingnya analisis data dalam bisnis
- Komponen Time Series: Trend, musiman, siklus, dan variasi acak
- Teknik Decomposisi Time Series: Decomposisi aditif dan multiplikatif
- Smoothing Techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing
- ARIMA Models: Autoregressive, Integrated, and Moving Average (ARIMA)
- Pemodelan Musiman (SARIMA): Seasonal ARIMA Models
- Data Analytics untuk Time Series: Penggunaan analitik data untuk eksplorasi time series
- Data Preparation: Persiapan data untuk analitik time series, pengolahan data hilang, outliers
- Forecasting dan Predictive Analytics: Teknik-teknik prediksi dengan data time series
- Machine Learning untuk Time Series: Penggunaan metode machine learning (misalnya Random Forest, Neural Networks) untuk prediksi data time series
- Model Evaluasi: Validasi model prediksi, cross-validation untuk time series
- Visualisasi Data Time Series: Teknik visualisasi untuk presentasi hasil analisis data
| - Introduction to Time Series and Data Analytics: Introduction to the concept of time series and the importance of data analysis in business
- Components of Time Series: Trend, seasonal, cyclical, and irregular variations
- Time Series Decomposition Techniques: Additive and multiplicative decomposition
- Smoothing Techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing
- ARIMA Models: Autoregressive, Integrated, and Moving Average (ARIMA)
- Seasonal Modeling (SARIMA): Seasonal ARIMA Models
- Data Analytics for Time Series: Using data analytics to explore time series data
- Data Preparation: Preparing data for time series analytics, handling missing data, and outliers
- Forecasting and Predictive Analytics: Prediction techniques with time series data
- Machine Learning for Time Series: Applying machine learning methods (e.g., Random Forest, Neural Networks) for time series prediction
- Model Evaluation: Validation of predictive models, cross-validation for time series
- Time Series Data Visualization: Visualization techniques to present time series analysis results
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Capaian Pembelajaran Mata Kuliah (CPMK) | - Mahasiswa mampu mengidentifikasi dan menganalisis komponen time series seperti tren, musiman, siklus, dan variasi acak dari berbagai dataset.
- Mahasiswa mampu menerapkan metode pemodelan deret waktu seperti ARIMA dan SARIMA untuk membuat prediksi berdasarkan data historis.
- Mahasiswa mampu mempersiapkan, membersihkan, dan mengolah dataset time series, termasuk penanganan data hilang dan outliers.
- Mahasiswa mampu menerapkan algoritma machine learning seperti Random Forest dan Neural Networks untuk memprediksi data time series secara akurat.
- Mahasiswa mampu memvisualisasikan hasil analisis time series secara efektif dan menyajikan informasi yang relevan untuk pengambilan keputusan bisnis.
| - Students are able to identify and analyze time series components such as trends, seasonality, cycles, and irregular variations from various datasets.
- Students are able to apply time series modeling methods such as ARIMA and SARIMA to make predictions based on historical data.
- Students are able to prepare, clean, and process time series datasets, including handling missing data and outliers.
- Students are able to apply machine learning algorithms, such as Random Forest and Neural Networks, to accurately predict time series data.
- Students are able to effectively visualize time series analysis results and present relevant information for business decision-making.
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Metode Pembelajaran | Perkuliahan, simulasi, diskusi kelas, studi kasus | Lectures, simulations, class discussions, case study |
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Modalitas Pembelajaran | Luring, sinkron, mandiri | Offline, synchronous, individual |
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Jenis Nilai | ABCDE |
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Metode Penilaian | Ujian akhir semester, proyek individual, tugas (individu dan kelompok) | Final test, individual project, assignment (individual and group) |
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Catatan Tambahan | | |
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