Implementation Of Triple Exponential Smoothing Method To Predict Palm Oil Production Of PT.Lonsum Web-Based

This research aims to develop a web-based palm oil (CPO) production forecasting system by applying the Triple Exponential Smoothing (TES) method to the production data of PT Lonsum Turangi. The data used includes 60 monthly data from 2020 to 2024. The first 36 data were used for model training, while the remaining 24 data were used for validation. Research instruments included semi-structured interviews and participatory observations to understand the operational patterns and needs of the system in the field. Triple Exponential Smoothing method was chosen for its ability to handle level, trend and seasonal components simultaneously, making it superior to other time series forecasting methods that require large volumes of data. The system was developed using the Rapid Application Development (RAD) method, PHP programming language, and MySQL database. The test results show a good level of prediction accuracy with a Mean Absolute Percentage Error (MAPE) value of 17.34% at an alpha value of 0.1. This system not only improves prediction accuracy, but also provides practical benefits in production planning, meeting market demand, and reducing potential losses due to production imbalances. The novelty of this research lies in the integration of the TES method into a web-based decision support system specific to the CPO industry.