House Price Prediction using the Random Forest Regression Algorithm

Abstract

Abstrak Prediksi harga rumah merupakan masalah kompleks karena dipengaruhi berbagai faktor seperti kualitas bangunan, lokasi, dan luas area tinggal sehingga metode konvensional sering kali kurang akurat dalam mengestimasi. Penelitian ini bertujuan menerapkan algoritma Random Forest Regression (RFR) untuk memprediksi harga rumah pada dataset House Prices-Advanced Regression Techniques dari Kaggle yang berisi 1.460 data properti. Metode SEMMA (Sample, Explore, Modify, Model, Assess) digunakan karena memiliki alur kerja yang sistematis dan memiliki fokus yang terstruktur sehingga model lebih dapat diandalkan. Pada tahap pemodelan, RFR diterapkan karena mampu menangani pola non-linier dan performanya tetap stabil pada jumlah fitur yang besar. Berdasarkan pengujian, model menghasilkan nilai Root Mean Squared Error (RMSE) sebesar 28.452,75 dan koefisien determinasi 89%, diikuti uji robustness sebesar 30.665,40 yang mengindikasikan stabilitas model. Analisis feature importance juga menunjukkan bahwa OverallQual memiliki pengaruh terbesar dalam penentuan harga. Temuan ini menegaskan bahwa RFR diandalkan untuk memprediksi harga rumah serta berpotensi untuk dikembangkan dalam sistem rekomendasi harga, penilaian properti otomatis, dan integrasi ke platform digital industri real estate. Kata kunci: harga rumah, random forest regression, R², RMSE Abstract 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.