Analisis Komparasi Kinerja ARIMA dan DES Holt Menggunakan Multi-Split Validation pada Peramalan Harga BBM Indonesia
DOI:
https://doi.org/10.62712/juktisi.v5i2.1367Keywords:
Peramalan Harga BBM, ARIMA, Double Exponential Smoothing Holt, Multi-Split Validation, Time Series, Mean Absolute Percentage ErrorAbstract
Harga Bahan Bakar Minyak (BBM) di Indonesia memiliki keterkaitan yang erat dengan pergerakan harga minyak mentah Brent Crude global, sehingga peramalan harga yang akurat menjadi penting dalam mendukung pengambilan keputusan dan perencanaan kebijakan energi. Penelitian ini bertujuan membandingkan kinerja metode Autoregressive Integrated Moving Average (ARIMA) dan Double Exponential Smoothing (DES) Holt dalam memprediksi harga BBM Indonesia menggunakan pendekatan Multi-Split Validation. Dataset yang digunakan adalah riyawat data harga BBM Indonesia periode Januari 2015 hingga April 2026 yang diperoleh dari Kaggle (World vs Asia Fuel Prices), terdiri atas 136 data bulanan. Penelitian menggunakan tiga skenario pembagian data latih dan data uji, yaitu 70:30, 80:20, dan 90:10. Proses pemodelan ARIMA dilakukan melalui uji stasioneritas, identifikasi parameter, Grid Search, dan uji diagnostik residual, sedangkan metode DES Holt menggunakan optimasi parameter alpha dan beta. Evaluasi kinerja model dilakukan menggunakan metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa metode ARIMA secara konsisten memberikan tingkat kesalahan prediksi yang lebih rendah dibandingkan DES Holt pada seluruh skenario pembagian data. Selain memiliki akurasi yang lebih baik, ARIMA juga menunjukkan kinerja yang lebih stabil terhadap perubahan proporsi data latih dan data uji. Dengan demikian, metode ARIMA lebih sesuai diterapkan pada peramalan harga BBM Indonesia pada dataset yang digunakan dalam penelitian ini.
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