The Moving Average Algorithm for Forecasting Palm Oil Fresh Fruit Bunch (FFB) Prices - Siregar, Zaid Ubay; Santoso, Heri (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2025)
Fresh Fruit Bunches (FFB) of oil palm are the main raw material in the production of Crude Palm Oil (CPO). Efforts to forecast FFB prices are crucial to help mitigate the negative impacts of price fluctuations, which are influenced by various external and internal factors. The problem addressed in this study is the fluctuation in FFB prices, which presents a major challenge for both farmers and stakeholders in the palm oil industry. The method used in this research is a quantitative approach with descriptive methods, applying the Moving Average algorithm. The results of the study show that the Moving Average algorithm is used to forecast FFB prices by calculating the average price over a specific time period. The accuracy of the forecast is evaluated using several metrics: first, the Root Mean Squared Error (RMSE) with a value of 137.19, indicating moderate forecasting error; second, the Mean Absolute Percentage Error (MAPE) with a value of 6.09%, indicating good accuracy; and third, the Mean Absolute Error (MAE) with a value of 117.0, indicating relatively small errors. The accuracy system is derived by comparing the forecasts with actual data and calculating metrics such as RMSE, MAPE, and MAE. These metrics help assess the accuracy of the FFB price forecasting model and allow for adjustments to improve forecasting results.