Comparison of the Results of the Weighted Moving Average Method and the Least Absolute Shrinkage and Selection Operator Method for Predicting Total Palm Oil Production at PT. Mora Niaga Jaya - Ardiansyah, Sakha; Dinata, Rozzi Kesuma; Ar Razi, Ar Razi (International Journal of Engineering, Science and Information Technology, 2025)
This study compares two prediction methods, Weighted Moving Average (WMA) and Least Absolute Shrinkage and Selection Operator (LASSO), in forecasting the total palm oil production at PT. Mora Niaga Jaya. Accurate forecasting is essential in the palm oil industry to support decision-making, optimize production planning, and manage supply chains efficiently. The WMA method produced more realistic prediction results, with a Mean Absolute Error (MAE) of 114,854 tons and a Mean Absolute Percentage Error (MAPE) of 220.45%, despite still having a considerable margin of error. These values suggest that while WMA is not perfectly accurate, it performs moderately well, given the complexity and variability inherent in agricultural production data. On the other hand, the LASSO method yielded significantly worse results, with an extremely high and unrealistic MAE and a MAPE of 291,456.000%, indicating that this approach is unsuitable for palm oil production forecasting in this specific case. The underperformance of the LASSO method may be due to the nature of the data used, which may not meet the assumptions required for LASSO to function optimally, such as linear relationships and minimal noise. This highlights the importance of aligning forecasting methods with the dataset's characteristics. Based on the comparison, it can be concluded that the WMA method is more appropriate for predicting palm oil production than LASSO. However, further steps such as parameter optimization, data normalization, and outlier removal should be undertaken to achieve better predictive accuracy. This research provides valuable insights into the importance of selecting the correct predictive method and ensuring data quality in forecasting. Ultimately, careful model selection and data preprocessing support effective operational and strategic decisions in the palm oil industry.