Oil Palm Price Prediction Using Holt-Winters Exponential Smoothing at PT Ivo Mas Tunggal - Christoper Jodi Aman Sinaga; Ari Purno Wahyu Wibowo (Yayasan Cita Cendekiawan Al Khwarizmi, 2026)
Fluctuations in oil palm Fresh Fruit Bunches (FFB) prices over time have created challenges in planning and decision-making processes within plantation companies, including at Nenggala Plantation of PT Ivo Mas Tunggal. Price instability affects production planning, marketing strategies, and risk management, making accurate forecasting essential for supporting managerial decisions-maing. Therefore, this study aims to predict oil palm Fresh Fruit Bunches (FFB) prices using the Holt-Winters Exponential Smoothing method and compare the performance of additive and multiplicative models. This study applies a time series forecasting using historical oil palm FFB price data from January 2020 to September 2024. The research process includes data preprocessing, splitting the dataset into training and testing sets, and forecasting using additive and multiplicative Holt-Winters models. Model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results showed that the additive Holt-Winters model achieved better forecasting accuracy than the multiplicative model, with a MAPE value of 10.08% compared to 11.98%. The forecasting results also indicated that the additive model was able to follow the historical trend and seasonal patterns of oil palm FFB prices effectively. The Holt-Winters Exponential Smoothing method with an additive approach is effective for predicting palm oil prices and can support production planning and managerial decision-making at the plantation level. Future studies are recommended to incorporate external variables and compare the Holt-Winters method with other forecasting approach to improve prediction performance.