Predicting House Prices in South Jakarta using Linear Regression and Decision Tree Regressor Methods

Abstract

The development of digital technology and data analytics has significantly improved predictive capabilities across various sectors, including the property market. House price prediction is a critical element in decision-making for buyers, sellers, investors, and developers, as prices are influenced by factors such as location, building size, land area, number of rooms, and available facilities. This study aims to build a house price prediction model in South Jakarta using Linear Regression and Decision Tree Regressor machine learning algorithms and compare their performance based on regression evaluation metrics. The dataset consists of 1010 entries, divided into 80% training data and 20% testing data. The experimental results show that Linear Regression produced the best performance with an R² score of 0.7713, meaning that the model can explain 77% of the variance in house prices. The model achieved a prediction error of approximately MAE Rp 1.98 billion and RMSE Rp 3.26 billion. Meanwhile, the Decision Tree Regressor obtained an R² score of 0.5560 with higher prediction errors, indicating a tendency toward overfitting and weaker generalization on testing data. Therefore, Linear Regression is recommended as the most effective approach for predicting property prices in South Jakarta and has the potential to be applied in decision-support systems for real estate market analysis.