Palm Oil Production Forecasting Using the SARIMA Model at the Terantam Plantation of PTPN IV Regional III in 2025

Eky; Gustriza Erda

doi:10.47194/orics.v6i4.430

Authors

Eky
eky.faradila1351@student.unri.ac.id
Universitas Riau
Gustriza Erda Statistics Study Program, Faculty of Mathematics and Natural Science, Riau University, Riau, Indonesia

Keywords:

Palm oil, forecasting, time series, SARIMA model

Abstract

Palm oil is one of the important plantation commodities that plays a major role in the Indonesian economy because it contributes to state revenues, making palm oil production crucial. Forecasting palm oil production is essential to support effective planning and decision-making in plantation management. This study aims to forecast palm oil production at the Terantam Plantation of PTPN IV Regional III for the year 2025 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The data used consist of monthly production data based on volume (kg) from January 2014 to December 2024. The results of the analysis indicate that the best model obtained is SARIMA(0,1,4)(0,1,1)12 with the smallest Akaike Information Criterion (AIC) value. Diagnostic tests show that the model residuals behave as white noise and are normally distributed, indicating that the model is suitable for forecasting. The Mean Absolute Percentage Error (MAPE) value of 8.02% indicates a very good level of accuracy. The forecasting results reveal a seasonal pattern in palm oil production, with the highest production in September 2025 amounting to 15,108,145 kg, and the lowest in February 2025 at 9,347,573 kg. Overall, the SARIMA model is able to capture both trend and seasonal patterns effectively, making the forecast results useful as a reference for production planning and operational management at the Terantam Plantation. Furthermore, the findings of this study are expected to serve as a reference for applying similar forecasting methods to other plantation commodities.

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