Prediksi Harga Rumah di Bandung 2024 Menggunakan Ensemble Learning: Analisis Komparatif dan Interpretabilitas

Muh Naufal Hibatulloh; Gilang Danu Prakoso; Adiestiana Dwi Putri Yunus; Tommy Dwi Putra

doi:10.30591/jpit.v10i2.8200

Prediksi Harga Rumah di Bandung 2024 Menggunakan Ensemble Learning: Analisis Komparatif dan Interpretabilitas

Muh Naufal Hibatulloh, Gilang Danu Prakoso, Adiestiana Dwi Putri Yunus, Tommy Dwi Putra

Abstract

House price prediction plays a crucial role in investment decision-making and financial planning, particularly in developing cities like Bandung with its complex property market dynamics. This study aims to evaluate and compare the performance of various ensemble learning techniques in predicting house prices in Bandung for the year 2024, with a specific focus on model interpretability analysis. The data was collected through web scraping from www.rumah123.com in March 2024, covering attributes such as location, number of rooms, land area, and building area. The evaluated ensemble techniques include Random Forest, Gradient Boosting Machines, Xtreme Gradient Boosting, Linear Regression, and Stacking Ensemble. Model performance was assessed using MAE, RMSE, and R-squared metrics, while interpretability analysis was conducted using SHAP values. The Model Stacking Ensemble shows the most optimal results with R² 0.9076, RMSE 0.311, and MAE 0.216 in experiments involving location features. Features such as land size, building size, and location have proven to have the greatest impact in predicting prices based on SHAP analysis. This model has been successfully integrated into a Flask website for interactive price predictions.

Keywords

Ensemble Learning; House Price Prediction; SHAP; Model Interpretability; Bandung.

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References

E. Febrion Rahayuningtyas, F. Novia Rahayu, Y. Azhar, and I. Artikel, “Prediksi Harga Rumah Menggunakan General Regression Neural Network,” JURNAL INFORMATIKA, vol. 8, no. 1, 2021, doi: 10.31294/ji.v8i1.9036.

M. Labib Mu’tashim, S. A. Damayanti, H. N. Zaki, T. Muhayat, and R. Wirawan, “Analisis Prediksi Harga Rumah Sesuai Spesifikasi Menggunakan Multiple Linear Regression,” vol. 3, p. 2021, doi: 10.52166/j-macc.v4i1.2406.

R. Mahendra Sanusi, A. Siswo, R. Ansori, R. Wijaya, and S. Si, “Prediksi Harga Rumah Di Kota Bandung Bagian Timur Dengan Menggunakan Metode Regresi Prediction Of House Prices In The East Bandung City Using The Regression Method.”

A. Fadilah, A. Siswo, R. Ansori, R. Wijaya, and S. Si, “Prediksi Harga Rumah Di Kota Bandung Bagian Timur Dengan Menggunakan Metode Moving Average Home Price Prediction In The East Bandung City With Moving Average.”

G. Najla, A. #1, and D. Fitrianah, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,” Jurnal Telematika, vol. 14, no. 2, doi: 10.61769/telematika.v14i2.321.

M. Radhi, D. Ryan Hamonangan Sitompul, S. Hamonangan Sinurat, and E. Indra, “Prediksi Harga Mobil Menggunakan Algoritma Regressi Dengan Hyper-Parameter Tuning,” Jurnal Sistem Informasi dan Ilmu Komputer Prima, vol. 4, no. 2, 2021.

N. Hadi and J. Benedict, “IMPLEMENTASI MACHINE LEARNING UNTUK PREDIKSI HARGA RUMAH MENGGUNAKAN ALGORITMA RANDOM FOREST,” 2024. [Online]. Available: https://www.kaggle.com/harlfoxem/housesalesprediction

I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” 2022, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2022.3207287.

M. Mamun, A. Farjana, M. Al Mamun, and M. S. Ahammed, “Lung cancer prediction model using ensemble learning techniques and a systematic review analysis,” in 2022 IEEE World AI IoT Congress, AIIoT 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 187–193. doi: 10.1109/AIIoT54504.2022.9817326.

U. Indahyanti, N. L. Azizah, and H. Setiawan, “Pendekatan Ensemble Learning Untuk Meningkatkan Akurasi Prediksi Kinerja Akademik Mahasiswa,” Jurnal Sains dan Informatika, vol. 8, no. 2, Dec. 2022, doi: 10.34128/jsi.v8i2.459.

M. Sadikin and F. Alfiandi, “Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, p. 4763, Dec. 2018, doi: 10.11591/ijece.v8i6.pp4763-4771.

J. Hong, H. Choi, and W. S. Kim, “A house price valuation based on the random forest approach: The mass appraisal of residential property in south korea,” International Journal of Strategic Property Management, vol. 24, no. 3, pp. 140–152, Mar. 2020, doi: 10.3846/ijspm.2020.11544.

E. Fitri, “Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah,” JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY (JACOST), vol. 4, no. 1, pp. 2723–1453, 2023, doi: 10.52158/jacost.491.

Y. Meng, N. Yang, Z. Qian, and G. Zhang, “What makes an online review more helpful: An interpretation framework using xgboost and shap values,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 3, pp. 466–490, 2021, doi: 10.3390/jtaer16030029.

S. M. Lundberg, G. G. Erion, and S.-I. Lee, “Consistent Individualized Feature Attribution for Tree Ensembles,” Feb. 2018, doi: 10.48550/arXiv.1802.03888 [Online]. Available: http://arxiv.org/abs/1802.03888

DOI: https://doi.org/10.30591/jpit.v10i2.8200