<![CDATA[The demand for housing continues to increase along with population growth. Predicting house prices is crucial to assist prospective buyers and investors in making more informed decisions. This study aims to predict house prices in the Jabodetabek area by comparing the performance of two machine learning algorithms, namely Extreme Gradient Boosting and Random Forest, to produce accurate price estimates. The prediction process includes data preprocessing, key variable exploration, and model evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). The results show that the Random Forest model performs best, with an MAE of 95,200,513.25, an MSE of 1.47e+19, and an R² of 0.77, outperforming the Extreme Gradient Boosting model with an MAE of 121,836,703.27, an MSE of 3.03e+19, and an R² of 0.52. Thus, this research is expected to serve as an effective tool for stakeholders in mitigating risks in property investment decisions in the Jabodetabek area.]]>
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