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All Journal JUSIFOR : Jurnal Sistem Informasi dan Informatika
Hendri Mahmud Nawawi
Universitas Nusa Mandiri, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Evaluasi Model ARIMA, Random Forest dan LSTM untuk Peramalan Harga Minyak Dunia Hendri Mahmud Nawawi; Agung Baitul Hikmah; Ali Mustopa; Fitra Septia Nugraha
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 5 No 1 (2026): JUSIFOR - Juni 2026
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v5i1.9370

Abstract

Global crude oil prices are a highly volatile economic indicator influenced by various dynamic factors, requiring forecasting methods capable of capturing both linear and non-linear patterns in time series data. This study aims to evaluate the performance of ARIMA, Random Forest, and LSTM models in predicting West Texas Intermediate (WTI) crude oil prices. The dataset consists of 1,316 daily closing price records from January 2020 to December 2024 obtained from Investing.com. The data were divided using a time-based split approach with a ratio of 80% for training and 20% for testing to preserve temporal characteristics. Model evaluation was conducted using MAE, MSE, RMSE, MAPE, and R-squared. The results indicate that LSTM achieved the best performance with an MAE of 2.914, RMSE of 3.66, MAPE of 0.854%, and an R² of 0.8514. In contrast, ARIMA and Random Forest produced negative R² values, indicating limitations in capturing the non-linear dynamics of oil prices. These findings confirm the effectiveness of deep learning approaches for crude oil price forecasting
Co-Authors Agung Baitul Hikmah Ali Mustopa Fitra Septia Nugraha