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All Journal Jurnal Sains Teknologi dan Sistem Informasi
Hasby Kuswanto
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Computer Science & IT

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Prediksi Nilai Ekspor Migas Indonesia menggunakan Metode ARIMA Hasby Kuswanto; Pradita Eko Prasetyo Utomo; Ulfa Khaira; Akhiyar Waladi
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.4103

Abstract

This study aims to predict Indonesia's oil and gas (migas) export values using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) methods. Time series data from Statistics Indonesia (BPS) was utilized to develop an optimal prediction model. The selected SARIMA model, SARIMA(1,1,1)(1,1,1,12), was chosen based on the lowest Akaike Information Criterion (AIC) value. Meanwhile, the LSTM model was developed to capture more complex patterns in time series data. The forecasting results indicate that the SARIMA model provides higher accuracy compared to LSTM based on the Mean Absolute Percentage Error (MAPE), although LSTM demonstrated lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). This study emphasizes that the choice of forecasting model should align with the characteristics of the data, where SARIMA is more suitable for oil and gas export data with seasonal patterns. These forecasting results can be utilized to support economic policy planning, optimize investments in the oil and gas sector, and mitigate global market fluctuation risks.
Co-Authors Akhiyar Waladi Khaira, Ulfa Utomo, Pradita Eko Prasetyo