Analisis Komparasi Algoritma Machine Learning untuk Prediksi Harga Sewa Properti Komersial: Comparative Analysis of Machine Learning Algorithms for Predicting Commercial Property Rental Prices

Dwi Juli Yantono Yantono; Nabila Rahmawati Puspaningrum; Salsabila Amani Zein; Adolfintje Hawila Amberam Winona Resky Marani; Gregorius Januario Namat

doi:10.57152/malcom.v6i1.2412

Authors

Dwi Juli Yantono Yantono Politeknik Keuangan Negara STAN
Nabila Rahmawati Puspaningrum Politeknik Keuangan Negara STAN
Salsabila Amani Zein Politeknik Keuangan Negara STAN
Adolfintje Hawila Amberam Winona Resky Marani Politeknik Keuangan Negara STAN
Gregorius Januario Namat Politeknik Keuangan Negara STAN

DOI:

https://doi.org/10.57152/malcom.v6i1.2412

Keywords:

Harga Sewa, Machine Learning, Properti Komersial, Random Forest, Tangerang Selatan

Abstract

Penentuan harga sewa properti komersial (ruko) di Kota Tangerang Selatan seringkali menghadapi kendala inefisiensi akibat metode penilaian yang subjektif dan heterogenitas pasar. Penelitian ini bertujuan untuk mengembangkan model prediksi harga sewa yang objektif dengan membandingkan tiga algoritma Machine Learning: Random Forest (RF), Extreme Gradient Boosting (XGBoost), dan Support Vector Regression (SVR). Dataset terdiri dari 275 data listing yang dikumpulkan melalui teknik web scraping dari platform Lamudi pada Desember 2025. Fitur yang digunakan meliputi luas bangunan dan lokasi (kecamatan). Hasil evaluasi menggunakan data testing menunjukkan bahwa Random Forest adalah model terbaik dengan skor R-Squared () sebesar 0,6801 dan Mean Absolute Percentage Error (MAPE) sebesar 27,40%. Sebaliknya, XGBoost dan SVR menunjukkan performa buruk dengan nilai negatif, mengindikasikan ketidakmampuan menangkap pola data secara efektif pada dataset berskala kecil. Analisis fitur penting (feature importance) mengungkapkan bahwa luas bangunan menjadi faktor paling dominan yang memengaruhi harga sewa dibandingkan lokasi. Penelitian ini membuktikan bahwa Random Forest merupakan metode yang robust untuk valuasi properti dalam konteks manajemen aset publik.

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References

Kosadata, "Pasar Properti di Kabupaten Tangerang dan Tangsel Tumbuh Signifikan," Kosadata.com, Feb. 12, 2025. [Online]. Available: [Link Berita]..

Badan Pusat Statistik Kota Tangerang Selatan, Kota Tangerang Selatan dalam angka 2025. Tangerang Selatan: BPS Kota Tangerang Selatan, 2025.

C. Lee, "Alleviating surveyor bias in real estate: An application to vacancy and property prices," International Journal of Strategic Property Management, vol. 28, no. 2, pp. 93–100, 2024.

D. Noviana, E. S. Ferista, B. T. Handayani, F. P. Adelia, and A. Setiawan, "Analisis strategi gaya negosiasi tarif sewa tanah PT KAI dengan warga di Kelurahan Sumbang, Kabupaten Bojonegoro," Kalijaga: Jurnal Penelitian Multidisiplin Mahasiswa, vol. 1, no. 2, pp. 33-40, 2024.

D. Prasdika, R. Auliyah, and A. R. Setiawan, "Menguak nilai dan makna di balik praktik penentuan harga sewa: Studi fenomenologis pada pengusaha kos-kosan," Jurnal InFestasi, vol. 14, no. 1, pp. 40-56, 2018.

F. D. Calainho, A. M. van de Minne, and M. K. Francke, "A machine learning approach to price indices: Applications in commercial real estate," The Journal of Real Estate Finance and Economics, vol. 68, pp. 624–653, 2024.

C. C. Afrisca, H. N. Rofiq, and D. D. Atmoko, "Penilaian properti: Penggunaan machine learning untuk prediksi nilai sewa," Jurnal Manajemen Keuangan Publik, vol. 8, no. 2, pp. 138-155, 2025..

I. L. Mulyahati, "Implementasi machine learning prediksi harga sewa apartemen menggunakan algoritma random forest melalui framework website Flask Python," Skripsi, Universitas Islam Indonesia, 2020.

Y. F. Munawar and A. Arisal, "Analisis prediksi harga sewa ruko menggunakan pendekatan machine learning," RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 3, pp. 2538-2544, 2025.

A. Siddiqui, G. Thekkayil, R. Verma, V. Verma, and R. Kaushik, "Property price prediction using machine learning," Journal of Information Systems Engineering and Management, vol. 10, no. 41s, 2025.

B. Wisnuadhi and I. Setiawan, "Rekomendasi fitur yang mempengaruhi harga sewa menggunakan pendekatan machine learning," Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 4, pp. 673-682, 2021.

A. G. Rawool, D. V. Rogye, S. G. Rane, and V. A. Bharadi, "House Price Prediction Using Machine Learning," Iconic Research and Engineering Journals, vol. 4, no. 11, pp. 29-33, 2021.

V. Gupta, A. Yadav, V. Nishad, and A. Haider, "House Price Prediction Using Machine Learning," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 2, pp. 175-180, 2024.

T. He, "Analysis and Prediction of Influencing Factors of American Real Estate Price Based on Machine Learning," in Proceedings of the 2nd International Conference on Financial Technology and Business Analysis, 2024, pp. 315-323.

C. Li, "House price prediction using machine learning," in Proceedings of the 4th International Conference on Signal Processing and Machine Learning, 2024, pp. 225-237.

K. Mahalakshmi, D. J. Priyan J, D. Raj N, and A. Aravind, "Enhancing House Price Predictability: A Comprehensive Analysis of Machine Learning Techniques for Real Estate and Policy Decision-Making," Data Analytics and Artificial Intelligence, vol. 4, no. 2, pp. 68-75, 2024.

T. Kee and W. K. O. Ho, "eXplainable Machine Learning for Real Estate: XGBoost and Shapley Values in Price Prediction," Civil Engineering Journal, vol. 11, no. 05, pp. 2116-2133, 2025.

I. Maulana, A. M. Siregar, S. A. P. Lestari, and S. Faisal, "Optimal Study of Real-Estate Price Prediction Models Using Machine Learning," Jurnal Teknik Informatika (JUTIF), vol. 5, no. 4, pp. 1149-1164, 2024..

M. Hernes, P. Tutak, and M. Siewiera, "Prediction of residential real estate price on primary market using machine learning," Procedia Computer Science, vol. 246, pp. 3142–3147, 2024..

L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

C. A. Zilli, L. C. Bastos, and L. R. da Silva, "Machine learning models in mass appraisal for property tax purposes: a systematic mapping study," Aestimum, vol. 84, pp. 31-52, 2024..

W. K. O. Ho, B. S. Tang, and S. W. Wong, "Predicting property prices with machine learning algorithms," Journal of Property Research, vol. 38, no. 1, pp. 48–70, 2020.

T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2016, pp. 785–794..

A. A. F. Oliveira, et al., "Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil," Aestimum, vol. 85, pp. 21-38, 2024.

H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, "Support vector regression machines," in Advances in Neural Information Processing Systems 9, MIT Press, 1997, pp. 155–161.

A. U. Akar and S. Yalpir, "Using SVR and MRA methods for real estate valuation in the smart cities," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVI-4/W5-2021, pp. 21–26, 2021..

N. Hadi and J. Benedict, "Implementasi machine learning untuk prediksi harga rumah menggunakan algoritma random forest," Computatio: Journal of Computer Science and Information Systems, vol. 8, no. 1, pp. 50-61, 2024.