Comparison of Multiple Linear Regression, Random Forest, and Decision Tree Methods for House Price Prediction in West Nusa Tenggara - Mindi Richia Putri; Asmaul Husna RS; Ahmad Fatoni Dwi Putra; Muhammad Ari Rifqi; Syaidatussalihah (Journal of Innovation Information Technology and Application (JINITA), 2026)
This study aims to develop an accurate housing price prediction model for West Nusa Tenggara (NTB) by comparing three machine learning approaches: Multiple Linear Regression (MLR), Random Forest (RF), and Decision Tree (DT). The research adopts a quantitative experimental design consisting of data collection through web scraping, preprocessing, model training, and performance evaluation. Secondary data were gathered from Lamudi, Rumah123, and 99.com, containing 641 housing records with key features such as land area, building area, number of bedrooms, number of bathrooms, and price. Data preprocessing involved handling missing values, normalization, and data augmentation to improve model robustness. Model performance was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The results show that non-linear models outperform linear ones, particularly after data augmentation. Without augmentation, Random Forest achieved the best performance (R² = 0.890, RMSE = 5.6×10⁹), while with augmentation, the Decision Tree model achieved the highest accuracy (R² = 0.999, RMSE = 1.1×10⁹). In contrast, MLR exhibited poor performance and sensitivity to data complexity. These findings conclude that tree-based models, particularly Decision Tree and Random Forest, provide superior prediction accuracy and adaptability for non-linear housing market data. The study contributes to the development of data-driven tools that can support policymakers, developers, and consumers in achieving more transparent and informed decision-making in the regional housing market.