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All Journal TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Nursuci Rafailah Arsya
Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang, Indonesia

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Analysis of the accuracy of the sarimax model in forecasting cocoa production in Central Sulawesi Nursuci Rafailah Arsya; Wellie Sulistijanti
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 1 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i1.1622

Abstract

Cocoa production in Central Sulawesi is among the highest in Indonesia and plays a crucial role in supporting national export needs. Cocoa production trends have shown a significant decline over the past two years. This decline is thought to be caused by various factors, including shrinking cultivated land due to land conversion and climate uncertainty, reflected in erratic rainfall patterns, resulting in unstable cocoa supply. Therefore, a scientific and data-driven approach to cocoa production forecasting is crucial for proper planning and monitoring of production and for anticipating imbalances between demand and supply. This study utilized the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) method, considered superior for its ability to capture seasonal patterns while simultaneously accommodating the influence of exogenous variables such as rainfall and land area, resulting in more accurate forecasting. The data used are cocoa production data as endogenous variables and rainfall and land area as exogenous variables for the period January 2020 to December 2023. The analysis stages include SARIMA model identification, pre-whitening, transfer function analysis, and evaluation of model accuracy using Mean Absolute Percentage Error (MAPE). The results of the study show that the best model is SARIMAX (1,1,1)(0,1,0), with land area variables at lag-5 being significant to cocoa production, producing a MAPE value of 3.29%, so this model can be used to predict future cocoa production.
Co-Authors Wellie Sulistijanti