Kurniasari, Dian and Shella, Tiara Pramay and Usman, Mustofa and Warsono, Warsono (2025) A Hybrid ARIMA–GRU Model for Forecasting Palm Oil Prices at PT SawitSumbermas Sarana in Central Kalimantan. Integra: Journal of Integrated Mathematics and Computer Science, 2 (1). pp. 7-14. ISSN 3109-1792

Text A Hybrid ARIMA–GRU Model for Forecasting Palm Oil Prices at PT SawitSumbermas Sarana in Central Kalimantan.pdf Download (3MB)

Abstract

The palm oil industry plays a strategic role in Indonesia’s economic landscape. As one of the world’s largest producers, Indonesia holds substantial potential in marketing both crude palm oil (CPO) and palm kernel oil on domestic and international fronts. Palm oil prices consistently correlate with CPO prices, given that the pricing of palm oil is benchmarked against CPO, resulting in market fluctuations. Forecasting future palm oil prices becomes an essential measure in response to this volatility. The ARIMA (Auto Regressive Integrated Moving Average) model has been widely recognized as a reliable method for time series forecasting. Despite its strengths, ARIMA faces challenges in identifying the non-linear components that are often present in real-world data. The Gated Recurrent Unit (GRU) model, which incorporates an update gate and a reset gate, offers an alternative that effectively captures complex non-linear patterns. A hybrid model integrating ARIMA and GRU has therefore been developed with the aim of improving predictive accuracy. This hybrid approach includes two stages: the ARIMA model for initial predictions and a GRU model that processes the residuals from the ARIMA output. In this study, the ARIMA-GRU hybrid model demonstrated strong performance, yielding a Mean Squared Error (MSE) of 868.4690, a Root Mean Squared Error (RMSE) of 29.4698, a Mean Absolute Percentage Error (MAPE) of 0.0117, and an overall accuracy of 99.9824%.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User:	DIAN KURNIASARI
Date Deposited:	17 Apr 2026 02:42
Last Modified:	17 Apr 2026 02:42
URI:	http://repository.lppm.unila.ac.id/id/eprint/54833

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