Comparative Analysis of K-Nearest Neighbors Algorithm and Random Forest Regressor for House Price Prediction in Bandung City
DOI:
https://doi.org/10.30871/jaic.v10i1.10718Keywords:
KNN Regressor, Random Forest Regressor, House Price Prediction, Machine Learning, Real Estate Valution, BandungAbstract
The rapid population growth and continuous urban expansion in Bandung have contributed to volatile and escalating housing prices, creating significant challenges for market transparency and affordability. This study aims to develop and evaluate machine-learning models to predict house prices in the Bandung region using a publicly available dataset consisting of 7,609 property records. Following the CRISP-DM methodology, the research includes data exploration, preprocessing (outlier handling using IQR, one-hot encoding, and feature standardization), model training, and performance evaluation. Two regression models K-Nearest Neighbors (KNN) Regressor and Random Forest (RF) Regressor—were compared through systematic hyperparameter tuning using Grid Search and Random Search techniques. The experimental results show that the Random Forest Regressor achieves the best performance with an R² score of 0.7838 and a mean absolute error (MAE) of approximately Rp 399.7 million, outperforming the optimized KNN model. Feature importance analysis also indicates that land area, building area, and location are the most influential predictors of property prices. The findings highlight the effectiveness of ensemble methods in handling complex real-estate data and demonstrate the potential of machine-learning-based predictive tools to support buyers, sellers, and policymakers in making informed and data-driven decisions in the Bandung housing market.
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