Forecasting Corn Production in Indonesia Using Holt–Winters Exponential Smoothing

Corn is a strategic agricultural commodity in Indonesia due to its important role in national food security and the livestock feed industry. However, national corn production exhibits strong seasonal fluctuations that pose challenges for production planning and policy formulation. This study aims to forecast national corn production in Indonesia using the Holt–Winters Triple Exponential Smoothing method. Monthly corn production data from January 2020 to October 2025 obtained from the Central Agency of Statistics (BPS) were analyzed. A multiplicative Holt–Winters model was applied to capture level, trend, and seasonal components in the data. Forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the proposed model achieved a MAPE value of 18.64%, indicating reasonable forecasting accuracy. The forecasting results for the period November 2025 to December 2026 reveal a stable seasonal pattern in national corn production. These findings demonstrate that the Holt–Winters method is a practical and effective approach for short- to medium-term forecasting and can support agricultural planning and decision-making related to national food security.