<![CDATA[Commodity price prediction is a vital aspect of supporting decision-making because price fluctuations can create uncertainty for businesses and stakeholders. This study aims to compare the performance of several machine learning algorithms for commodity price prediction, namely Random Forest, XGBoost, Support Vector Regression (SVR), Gradient Boosting, and Stacking Ensemble. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results show that Gradient Boosting achieved the best overall performance, with an RMSE of 219.07 and an R² of 0.9968, while Random Forest had the lowest MAE of 65.98. The Stacking Ensemble also demonstrated competitive performance, achieving an RMSE of 253.07 and an R² of 0.9958. In contrast, SVR produced the lowest performance, with an RMSE of 1,919.65 and an R² of 0.7555. Based on these results, Gradient Boosting was selected as the most optimal model because it provided the lowest prediction error and the highest ability to explain data variation. These findings demonstrate that selecting an appropriate machine learning algorithm plays a crucial role in improving commodity price prediction performance.]]>
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