Analisis Prediksi Harga Sewa Ruko Menggunakan Pendekatan Machine Learning

Yunadi Fitra Munawar; Andria Arisal

doi:10.31004/riggs.v4i3.2184

Authors

Yunadi Fitra Munawar Universitas Padjadjaran
Andria Arisal Universitas Padjadjaran

DOI:

https://doi.org/10.31004/riggs.v4i3.2184

Keywords:

Harga Sewa Ruko, Prediksi Properti, Machine Learning, Random Forest, SVR, XGBoost

Abstract

Penelitian ini mengembangkan sistem prediksi harga sewa ruko menggunakan pendekatan machine learning di tiga kota besar di Indonesia: Jakarta, Semarang, dan Surabaya. Ruko merupakan komponen penting dalam pasar properti komersial di Indonesia. Penentuan harga sewa yang akurat sangat dibutuhkan oleh pemilik properti, investor, maupun pemerintah daerah. Penelitian ini menggunakan tiga algoritma machine learning: Random Forest, Support Vector Regression (SVR), dan XGBoost. Data diperoleh dari hasil web scraping situs properti online dan diperkaya dengan variabel tambahan seperti kepadatan penduduk. Evaluasi dilakukan menggunakan MAE, MAPE, RMSE, dan R² Score. Hasilnya, SVR menunjukkan kinerja terbaik di Semarang dan Surabaya, sementara XGBoost unggul di Jakarta. Agar dapat digunakan secara luas, model terbaik diintegrasikan ke dalam aplikasi web sederhana berbasis Streamlit. Pengguna cukup memasukkan detail properti, dan sistem akan memberikan estimasi harga sewa secara langsung. Aplikasi ini memberikan kemudahan dalam penilaian harga sewa yang cepat dan objektif, serta mendukung pengambilan keputusan berbasis data di sektor properti Indonesia.

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