Predicting Greater Jakarta Area House Prices Using Random Forest and Linear Regression

Firli Firmansyah Agustin; Fariz Nur Fikri Zaki

doi:10.35671/jmtt.v4i2.104

Authors

Firli Firmansyah Agustin Universitas Amikom Purwokerto
Fariz Nur Fikri Zaki Universitas Amikom Purwokerto

DOI:

https://doi.org/10.35671/jmtt.v4i2.104

Keywords:

Price Prediction, Machine Learning, Random Forest, Multiple Linear

Abstract

This study focuses on analyzing and predicting house prices in the Greater Jakarta Area using a machine learning approach, specifically comparing the performance of random forest regression and multiple linear regression. the increasing demand for adequate housing in Greater Jakarta Area, coupled with fluctuating house prices influenced by factors like land size, building size, number of bedrooms, bathrooms, and other facilities, necessitates an accurate price prediction system to assist both the public and businesses in decision-making. data was collected from Rumah123.com via Kaggle, followed by pre-processing and exploratory data analysis (EDA). the models were built using both algorithms and evaluated through 10-fold cross-validation, with an 80% training and 20% testing data split. the results demonstrate that random forest regression outperforms multiple linear regression, achieving a correlation coefficient of 0.5043 and a mean absolute error of 157,698,532. in contrast, multiple linear regression (m5p) yielded a correlation coefficient of 0.4895 and a mean absolute error of 209,890,933. therefore, random forest regression is recommended as a superior model for house price prediction in the Greater Jakarta Area region.

Downloads

Download data is not yet available.

References

D. Aqsha, “A Comparative Analysis of Extreme Gradient Boosting and Random Forest Algorithms for House Price Prediction in the Greater Jakarta Area,” J. Ilmu Komput. dan Sist. Inf., vol. 13, no. 1, Jan. 2025, doi: 10.24912/jiksi.v13i1.32863.

L. El Mouna, H. Silkan, Y. Haynf, M. F. Nann, and S. C. K. Tekouabou, “A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms,” E3S Web Conf., vol. 418, p. 03001, Aug. 2023, doi: 10.1051/e3sconf/202341803001.

M. N. Hibatulloh, G. D. Prakoso, A. D. Putri Yunus, and T. D. Putra, “Predicting House Prices in Bandung 2024 Using Ensemble Learning: A Comparative and Interpretability Analysis,” J. Inform. J. Pengemb. IT, vol. 10, no. 2, pp. 484–493, Apr. 2025, doi: 10.30591/jpit.v10i2.8200.

L. Matic and Z. Kalinić, “HOUSING PRICE PREDICTION USING XGBOOST AND RANDOM FOREST METHODS,” in ZBORNIK RADOVA, Faculty of Economics, Kragujevac, 2025, pp. 417–424. doi: 10.46793/EBM24.417M.

C. Zou, “The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China,” Highlights Sci. Eng. Technol., vol. 39, pp. 327–333, Apr. 2023, doi: 10.54097/hset.v39i.6549.

P. Mahajan, M. Gawade, A. Patel, S. Barhanpurkar, and O. Deshmukh, “House Price Prediction and Recommendation,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 4, pp. 2009–2013, Apr. 2023, doi: 10.22214/ijraset.2023.49350.

I. R. Ningsih, A. Faqih, and A. R. Rinaldi, “House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Greater Jakarta Area Area,” J. Artif. Intell. Eng. Appl., vol. 4, no. 2, pp. 687–694, Feb. 2025, doi: 10.59934/jaiea.v4i2.733.

D. J. C. Sihombing, “Application of Feature Engineering Techniques and Machine Learning Algorithms for Property Price Prediction,” JITSI J. Ilm. Teknol. Sist. Inf., vol. 5, no. 2, pp. 72–76, Jun. 2024, doi: 10.62527/jitsi.5.2.241.

C. Li, “House price prediction using machine learning,” Appl. Comput. Eng., vol. 53, no. 1, pp. 225–237, Mar. 2024, doi: 10.54254/2755-2721/53/20241426.

L. Fang, “Machine learning models for house price prediction,” Appl. Comput. Eng., vol. 4, no. 1, pp. 409–415, May 2023, doi: 10.54254/2755-2721/4/20230505.

H. Sharma, H. Harsora, and B. Ogunleye, “An Optimal House Price Prediction Algorithm: XGBoost,” Analytics, vol. 3, no. 1, pp. 30–45, Jan.2024, doi: 10.3390/analytics3010003.

N. P. Ariyanti, Agung Triayudi, and Ratih Titi Komala Sari, “Analysis of K-NN Algorithm and Linear Regression to Predict House Prices in Greater Jakarta Area,” SaNa J. Blockchain, NFTs Metaverse Technol., vol. 2, no. 1, pp. 65–71, Feb. 2024, doi: 10.58905/sana.v2i1.265.

J. Hao, “Housing Price Prediction Model and Impact Factors Analysis,” Highlights Sci. Eng. Technol., vol. 39, pp. 1017–1023, Apr. 2023, doi:10.54097/hset.v39i.6696.

H. Li, “House Price Prediction and Analysis Based on Random Forest and XGBoost Models,” Highlights Business, Econ. Manag., vol. 21, pp. 934– 938, Dec. 2023, doi: 10.54097/hbem.v21i.14837.

A. Deaconu, A. Buiga, and H. Tothăzan, “REAL ESTATE VALUATION MODELS PERFORMANCE IN PRICE PREDICTION,” Int. J. Strateg. Prop. Manag., vol. 26, no. 2, pp. 86–105, Feb. 2022, doi: 10.3846/ijspm.2022.15962.