<![CDATA[Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees.]]>
