<![CDATA[Determining the selling price of a house is a crucial aspect in property transactions, especially in regions with dynamic market conditions such as Yogyakarta. This study compares two predictive modeling approaches Linear Regression and Random Forest Regressor in estimating house prices based on property data obtained from the rumah123.com website. The dataset used consists of 1,036 entries, covering variables such as price, land area, building area, number of bedrooms, number of bathrooms, availability of a carport, and location. After undergoing data preprocessing, both models were trained and tested using the same dataset to assess their predictive performance. Evaluation results indicate that the Random Forest model outperforms Linear Regression in terms of accuracy, particularly in handling data variation and non-linear relationships between variables. Although Linear Regression produced a coefficient of determination (R²) of 0.846 indicating that the model could explain 84.6% of the variability in house prices Random Forest demonstrated more precise predictions on the test data. These findings emphasize that selecting the appropriate model depends heavily on the complexity of the data and the required level of accuracy. This study provides a valuable contribution to the development of data-driven decision support systems for property price estimation and serves as a foundation for further research using more advanced machine learning approaches.]]>
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