<![CDATA[House price prediction plays a crucial role in investment decision-making and financial planning, particularly in developing cities like Bandung with its complex property market dynamics. This study aims to evaluate and compare the performance of various ensemble learning techniques in predicting house prices in Bandung for the year 2024, with a specific focus on model interpretability analysis. The data was collected through web scraping from www.rumah123.com in March 2024, covering attributes such as location, number of rooms, land area, and building area. The evaluated ensemble techniques include Random Forest, Gradient Boosting Machines, Xtreme Gradient Boosting, Linear Regression, and Stacking Ensemble. Model performance was assessed using MAE, RMSE, and R-squared metrics, while interpretability analysis was conducted using SHAP values. The Model Stacking Ensemble shows the most optimal results with R² 0.9076, RMSE 0.311, and MAE 0.216 in experiments involving location features. Features such as land size, building size, and location have proven to have the greatest impact in predicting prices based on SHAP analysis. This model has been successfully integrated into a Flask website for interactive price predictions.]]>
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