Implementasi Algoritma Linear Regression Untuk Prediksi Harga Rumah di Daerah Tebet

Aditya  Pratama; Adha  Maulana; Rendy Amy  Saputra

doi:10.46229/jifotech.v5i1.986

Authors

Aditya Pratama Universitas Nahdlatul Ulama Kalimantan Barat
Adha Maulana Universitas Nahdlatul Ulama Kalimantan Barat
Rendy Amy Saputra STMIK PONTIANAK

DOI:

https://doi.org/10.46229/jifotech.v5i1.986

Keywords:

House Price Prediction, Linear Regression, Property, Tebet, Machine Learning

Abstract

This research focuses on the issue of house price variation in the Tebet area, South Jakarta, influenced by factors such as land area, building area, number of bedrooms, number of bathrooms, and additional facilities. This price variability makes it difficult for potential buyers and sellers to determine a fair price, potentially resulting in poor decisions and financial losses. Therefore, the main objective of this research is to develop a house price prediction model using the linear regression algorithm, which is expected to provide a more accurate price estimate based on relevant features.as a solution, this research applies the linear regression algorithm to analyze and predict house prices, providing useful information for stakeholders, including buyers, sellers, and real estate agents. The methods used include collecting house price data and related features from the Rumah123 property platform, which are then pre-processed by dividing them into training data (80%) and test data (20%). The model is evaluated using the Mean Squared Error (MSE) and R-squared metrics. The research results show that this model has an R-squared of 0.7713, which means it can explain about 77% of the variation in house prices. This model also predicts the price of a new house with certain features, such as a building area of 100 m² and a land area of 300 m², at around IDR 8,555,000,000. This research is expected to make a significant contribution to understanding the dynamics of house prices in Tebet.

References

Z. Zhang, “Decision Trees for Objective House Price Prediction,” in 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2021, pp. 280–283, doi: 10.1109/MLBDBI54094.2021.00059.

Chairina, R. Widyasari, and S. M. A. Putri, “Hybrid Naive Bayes Classifier and Markov Chain for Predicting the Level of Quick Response Indonesian Standard (QRIS) Usage,” J. Inf. Technol., vol. 4, no. 1, pp. 150–156, 2024, doi: https://doi.org/10.46229/jifotech.v4i1.875.

L. Rampini and F. R. Cecconi, “Artificial Intelligence Algorithms to Predict Italian Real Estate Market Prices,” J. Prop. Invest. Financ., vol. 40, no. 6, pp. 588–611, 2022, doi: https://doi.org/10.1108/jpif-08-2021-0073.

P. Chandu and N. B. Devi, “Improved Prediction Accuracy of House Price Using Decision Tree Algorithm over Linear Regression Algorithm,” in 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 2023, pp. 1–6, doi: 10.1109/ICONSTEM56934.2023.10142280.

A. Sul, V. Jagtap, P. Jesalpura, A. Nema, and R. R, “Optimizing House Price Prediction: Comparative Analysis of Machine Learning Techniques,” in 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 2024, pp. 1–7, doi: 10.1109/ICEEICT61591.2024.10718610.

M. Cekic, K. N. Korkmaz, H. Mukus, A. A. Hameed, A. Jamil, and Faezeh, “Artificial Intelligence Approach for Modeling House Price Prediction,” 2022, doi: 10.1109/ICMI55296.2022.9873784.

H. Li, “House Price Prediction Based on Machine Learning,” in Proceedings of the 3rd International Conference on Signal Processing and Machine Learning, 2023, pp. 623–628, doi: 10.54254/2755-2721/4/2023362.

S. Sharma, D. Arora, G. Shankar, P. Sharma, and V. Motwani, “House Price Prediction using Machine Learning Algorithm,” in 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 2023, pp. 982–986, doi: 10.1109/ICCMC56507.2023.10084197.

B. Sonare, S. Thepade, P. Gangane, E. Govardhan, T. Nawani, and P. Gadkar, “Going the distance: Application of Machine Learning Algorithms in predicting Housing Price Dynamics,” in 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST), 2024, pp. 1–4, doi: 10.1109/ICCIGST60741.2024.10717472.

M. Thamarai and S. Malarvizhi, “House Price Prediction Modeling Using Machine Learning,” Int. J. Inf. Eng. Electron. Bus., vol. 12, no. 2, pp. 15–20, 2020, doi: 10.5815/ijieeb.2020.02.03.

R. A. Saputra and A. Pratama, “IMPLEMENTASI DECISION TREE UNTUK PREDIKSI HARGA RUMAH DI DAERAH TEBET,” J. Inf. Syst. Manag., vol. 6, no. 2, pp. 164–170, 2025, doi: https://doi.org/10.24076/joism.2025v6i2.1928.

J. J. Guo, “House Rent Prediction Method Based on Decision Tree: Take India as an Example,” in 5th International Conference on Global Economy, Finance and Humanities Research (GEFHR 2023), 2023, pp. 666–671, doi: 10.54097/hbem.v21i.14722.

P. A. Kumar, H. Rawat, and Y. Singh, “Effective Machine Learning Algorithm to Predict House Price,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 11, pp. 1741–1744, 2023, doi: 10.22214/ijraset.2023.56935.

X. Li, “Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model,” Comput. Intell. Neurosci., vol. 2023, no. 1, 2023, doi: https://doi.org/10.1155/2022/3590224.

F. Martinez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Trans. Knowl. Data Eng., vol. 33, no. 8, pp. 3048–3061, 2021, doi: 10.1109/TKDE.2019.2962680.