<![CDATA[In this paper, we forecast coffee production in Aceh Province, Indonesia, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Coffee is a critical commodity for the region’s economy, contributing significantly to both local income and export revenues. Accurate forecasting of coffee production is essential for economic planning, supply chain management, and strategic development in the coffee sector. Using secondary data from the Indonesian Central Bureau of Statistics, we identified the SARIMA (2,0,1)(1,1,1)12 model as the best fit, with a Mean Absolute Percentage Error (MAPE) of 12.94%. The forecasting results for the period of 2023 to 2024 reveal a consistent seasonal pattern in line with historical data, though a slight decline in production is projected. Notably, the lowest production of 22 tons occurred in February 2019, while the highest, 21,408 tons, was recorded in July 2019. These findings provide valuable insights for policymakers and stakeholders in the coffee industry, offering a robust basis for developing targeted interventions to enhance production and manage fluctuations. The results underscore the importance of reliable forecasting models like SARIMA in supporting sustainable growth and decision-making in regional agricultural sectors.]]>
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