House Price Prediction using the Random Forest Regression Algorithm - Fika Halimah Balqis; Qurrotul Aini (SISTEMASI, 2026)
House price prediction is a complex problem because it is influenced by various factors such as building quality, location, and living area size. As a result, conventional methods often lack accuracy in estimating housing prices. This study aims to apply the Random Forest Regression (RFR) algorithm to predict house prices using the House Prices – Advanced Regression Techniques dataset from Kaggle, which contains 1,460 property records. The SEMMA (Sample, Explore, Modify, Model, Assess) methodology was adopted due to its systematic workflow and structured focus, which improves the reliability of the developed model. In the modeling stage, RFR was implemented because it is capable of handling non-linear patterns and maintains stable performance even with a large number of features. Based on the evaluation results, the model achieved a Root Mean Squared Error (RMSE) of 28,452.75 and a coefficient of determination (R²) of 89%. This was followed by a robustness test with an RMSE of 30,665.40, indicating the stability of the model. Feature importance analysis also revealed that OverallQual had the greatest influence on house price prediction. These findings confirm that Random Forest Regression is a reliable method for predicting house prices and has strong potential to be further developed for price recommendation systems, automated property valuation, and integration into digital platforms within the real estate industry.