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All Journal Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Roberto Reyna Wijaya
Universitas Telkom
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Optimasi Random Forest untuk Prediksi Harga Rumah menggunakan Seleksi Fitur dan Penyetelan Hyperparamater Roberto Reyna Wijaya; Muhammad Eka Purbaya; Rozzan Zhofrul Islam
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 5 No. 2 (2026): Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v5i2.6237

Abstract

House price prediction has become an important need in the property sector because selling prices are influenced by various factors, such as building quality, living area size, garage capacity, and physical property conditions. The complexity of relationships among these variables makes machine learning approaches, particularly Random Forest Regression, relevant for developing accurate and stable prediction models. This article discusses the optimization of Random Forest Regression for house price prediction through feature selection and hyperparameter tuning. The dataset used in this study is the House Prices: Advanced Regression Techniques dataset from Kaggle, with SalePrice as the target variable. The analysis process includes data exploration, data preprocessing, feature selection, train-test data splitting, Random Forest model development, and hyperparameter optimization using Randomized Search. Model performance was evaluated using R-Squared, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results show that the optimized model achieved an R-Squared value of 0.8927 and an RMSE value of 28,687.08. The most dominant features influencing house prices were OverallQual, GrLivArea, and GarageCars. Although the performance improvement after optimization was relatively modest, Random Forest proved capable of producing stable predictions and can be used as a decision-support tool in the property business. However, the model still has limitations in predicting extreme prices in the luxury housing segment.
Co-Authors Muhammad Eka Purbaya Rozzan Zhofrul Islam