<![CDATA[The demand for temporary housing in Jimbaran continues to increase, making it more complex to determine boarding house prices due to various physical characteristics, location, and facilities. Previous studies have limitations in modeling nonlinear relationships among variables and have not integrated feature engineering and hyperparameter tuning to improve prediction performance. This study aims to develop a model for predicting boarding house prices using the decision tree regression algorithm. The model was developed through data preprocessing, feature engineering, and hyperparameter tuning using GridSearchCV with stratified K-fold cross-validation. The optimal hyperparameter combination was max_depth = 8, min_samples_split = 5, min_samples_leaf = 2, and max_leaf_nodes = 80. The model achieved R² values of 0.8871 for the training data and 0.8326 for the testing data, with an MAE of IDR 225,438 and an RMSE of IDR 303,080. The R² difference of 0.0546 indicates satisfactory generalization without significant overfitting. In addition to producing an accurate prediction model, this study contributes through the implementation of data preprocessing, feature engineering, hyperparameter tuning, and feature importance analysis to identify the factors that most significantly affect boarding house prices.]]>
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