Analisis Kinerja Model Machine Learning untuk Memprediksi Data Deret Waktu: Sebuah Tinjaun
DOI:
https://doi.org/10.31004/riggs.v5i2.11037Keywords:
Data Deret Waktu, Machine Learning, Deep Learning, Model Hybrid, PrediksiAbstract
Penelitian ini bertujuan untuk menganalisis kinerja model Machine Learning dan Deep Learning dalam memprediksi data deret waktu (time series) melalui pendekatan Systematic Literature Review (SLR). Kajian dilakukan terhadap 27 artikel ilmiah yang dipublikasikan pada periode 2020–2026 dan diperoleh dari berbagai basis data bereputasi, seperti Scopus, IEEE Xplore, ScienceDirect, MDPI, dan Google Scholar. Penelitian ini difokuskan pada identifikasi algoritma yang paling dominan digunakan, perbandingan kinerja antara model tunggal dan model hybrid, serta faktor-faktor yang memengaruhi peningkatan performa prediksi. Hasil kajian menunjukkan bahwa algoritma Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), dan Extreme Gradient Boosting (XGBoost) merupakan metode yang paling banyak digunakan karena mampu menangani pola temporal yang kompleks dan dinamis. LSTM unggul dalam menangkap ketergantungan jangka panjang, GRU menawarkan efisiensi komputasi yang lebih baik dengan tingkat akurasi yang sebanding, sedangkan XGBoost memiliki stabilitas tinggi dan kemampuan yang baik dalam mengatasi overfitting. Selain itu, model hybrid seperti CNN-LSTM, ARIMA-LSTM, Wavelet-GRU, dan Autoencoder-CNN-LSTM secara konsisten menunjukkan kinerja yang lebih unggul dibandingkan model tunggal berdasarkan metrik evaluasi RMSE, MAE, MAPE, dan SMAPE. Faktor-faktor seperti rekayasa fitur, pra-pemrosesan data, reduksi noise, optimasi hiperparameter, serta strategi ensemble dan hybrid terbukti berperan penting dalam meningkatkan akurasi prediksi. Dengan demikian, pendekatan hybrid dinilai lebih efektif dan andal dalam menangani karakteristik data deret waktu yang kompleks, nonlinier, volatil, dan mengandung ketidakpastian tinggi pada berbagai bidang aplikasi modern.
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Copyright (c) 2026 Irsan Hanafiah, Regiolina Hayami
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