COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES
DOI:
https://doi.org/10.33480/jitk.v11i2.7256Keywords:
housing, PCA, prediction, price, random forestAbstract
House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)
Downloads
References
F. C. K. Analisa and S. Okada, “Tiny house characteristics in Indonesia based on millennial’s user preference,” Urban, Plan. Transp. Res., vol. 11, no. 1, pp. 1–25, 2023, doi: 10.1080/21650020.2023.2166095.
A. Barlybayev, A. Sankibayev, R. Niyazova, and G. Akimbekova, “Machine learning for real estate valuation: Astana, Kazakhstan case,” Indones. J. Electr. Eng. Comput. Sci., vol. 35, no. 2, pp. 1110–1121, 2024, doi: 10.11591/ijeecs.v35.i2.pp1110-1121.
Y. Lu, V. Shi, and C. J. Pettit, “The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia,” ISPRS Int. J. Geo-Information, vol. 12, no. 7, 2023, doi: 10.3390/ijgi12070298.
L. G. Perdamaian and Z. (John) Zhai, “Status of Livability in Indonesian Affordable Housing,” Architecture, vol. 4, no. 2, pp. 281–302, 2024, doi: 10.3390/architecture4020017.
A. M. Igamo, A. Azwardi, A. Saputra, R. G. Ismail, G. Gustriani, and V. D. Melliny, “Monetary Policy and Demographics: Empirical Evidence for Housing Prices in Indonesia,” Sriwij. Int. J. Dyn. Econ. Bus., vol. 6, no. 4, pp. 371–384, 2023, doi: 10.29259/sijdeb.v6i4.371-384.
H. Sharma, H. Harsora, and B. Ogunleye, “An Optimal House Price Prediction Algorithm: XGBoost,” Analytics, vol. 3, no. 1, pp. 30–45, 2024, doi: 10.3390/analytics3010003.
E. B. Satoto, “Boosting Homeownership Affordability for Low-Income Communities in Indonesia,” Int. J. Sustain. Dev. Plan., vol. 18, no. 5, pp. 1365–1376, 2023, doi: 10.18280/ijsdp.180506.
N. Dhaka, A. Chaudhary, D. Sisodia, M. Sharma, and S. Babu, “Prediction of House Pricing Using Machine Learning,” Tuijin Jishu/Journal Propuls. Technol., vol. 45, no. 2, pp. 1026–1034, 2024, doi: 10.1109/ICAC3N60023.2023.10541549.
K. Srivastava, S. Verma, M. S. Khan, and A. Singh, “House Price Prediction Using Machine Learning,” in Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, 2021, pp. 203–206. doi: 10.1109/ICAC3N53548.2021.9725552.
E. sakti Pramukantoro, K. Amron, V. Wardhani, and P. A. Kamila, “Implementasi Sensor Polar H10 dan Raspberry Pi dalam Pemantauan dan Klasifikasi Detak Jantung Beberapa Individu Secara Simultan dengan Pendekatan Machine Learning ,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 1, pp. 175–182, 2024, doi: 10.25126/jtiik.20241117716.
Wiharto and F. N. Mufidah, “Early detection of coronary heart disease based on risk factors using interpretable machine learning,” Int. J. Adv. Appl. Sci., vol. 13, no. 4, pp. 944–956, 2024, doi: 10.11591/ijaas.v13.i4.pp944-956.
E. Pitaloka, T. B. A. Hartanto, and S. Sandiwarno, “Penerapan Machine Learning Untuk Prediksi Bencana Banjir,” J. Sist. Inf. Bisnis, vol. 14, no. 1, pp. 62–76, 2024, doi: 10.21456/vol14iss1pp62-76.
R. Kosasih, Sudaryanto, and A. Fahrurozi, “Classification of six banana ripeness levels based on statistical features on machine learning approach,” Int. J. Adv. Appl. Sci., vol. 12, no. 4, pp. 317–326, 2023, doi: 10.11591/ijaas.v12.i4.pp317-326.
Nimatul Mamuriyah, Richard, and Haeruddin, “Implementation Mean Imputation and Outlier Detection for Loan Prediction Using the Random Forest Algorithm,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 4, pp. 937–944, 2025, doi: 10.33480/jitk.v10i4.6437.
H. A. Setyadi, Supriyanta, G. S. Nurohim, P. Widodo, and Y. Sutanto, “Knowledge-Based Intelligent System for Diagnosing Three-Wheeled Motorcycle Engine Faults,” Int. J. Informatics Vis., vol. 8, no. 4, pp. 2472–2478, 2024, doi: 10.62527/joiv.8.4.2487.
J. N. Sari, P. Madona, H. Kusryanto, M. M. Zain, and M. Valzon, “Electrocardiogram signals classification using random forest method for web-based smart healthcare,” Int. J. Adv. Appl. Sci., vol. 12, no. 2, pp. 133–143, 2023, doi: 10.11591/ijaas.v12.i2.pp133-143.
J. M. Alyza, F. S. Utomo, Y. Purwati, B. A. Kusuma, and M. S. Azmi, “Music Recommendation System Based on Cosine Similarity and Supervised Genre Classification,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 9, no. 1, pp. 77–80, 2023, doi: 10.33480/jitk.v9i1.4324.
E. A. Winanto et al., “Peningkatan Performa Deteksi Serangan Menggunakan Metode PCA Dan Random Forest,” vol. 11, no. 2, pp. 285–290, 2024, doi: 10.25126/jtiik.20241127678.
J. Chen, F. Gong, S. Xiang, and T. Yu, “Application of principal component analysis in evaluation of epidemic situation policy implementation,” J. Phys. Conf. Ser., vol. 1903, no. 1, pp. 1–5, 2021, doi: 10.1088/1742-6596/1903/1/012056.
R. Tanamal, N. Minoque, T. Wiradinata, Y. Soekamto, and T. Ratih, “House Price Prediction Model Using Random Forest in Surabaya City,” TEM J., vol. 12, no. 1, pp. 126–132, 2023, doi: 10.18421/TEM121-17.
R. Jáuregui-Velarde, L. Andrade-Arenas, D. H. Celis, R. C. Dávila-Morán, and M. Cabanillas-Carbonell, “Web Application with Machine Learning for House Price Prediction,” Int. J. Interact. Mob. Technol., vol. 17, no. 23, pp. 85–104, 2023, doi: 10.3991/IJIM.V17I23.38073.
Z. A. Jasim, Z. Zahid, A. Z. Ul-Saufie, and M. M. Mansor, “Comparison Between Principal Component Analysis and Sparse Principal Component Analysis as Dimensional Reduction Techniques for Random Forest based High Dimensional Data Classification,” in 2024 IEEE International Conference on Computing, ICOCO 2024, IEEE, 2024, pp. 7–11. doi: 10.1109/ICOCO62848.2024.10928248.
O. Ben Ali, S. Hammami, M. Hasni, F. H’Mida, and A. N. S. Moh, “Using Machine Learning To Evaluate Industry 4.0 Maturity: A Comprehensive Analysis Highlighting Lean’s Impact On Digital Transformation,” J. Eng. Technol. Ind. Appl., vol. 10, no. 5, pp. 156–167, 2024, doi: https://doi.org/10.5935/jetia.v10i50.1262.
S. M. S. Zulkiplee, M. A. M. Shukran, M. R. M. Isa, M. A. Khairuddin, N. Wahab, and H. Hidayat, “Examining the Impact Factors Influencing Higher Education Institution (HEI) Students’ Security Behaviours in Cyberspace Environment,” Int. J. Informatics Vis., vol. 9, no. 1, pp. 146–152, 2025, doi: 10.62527/joiv.9.1.2296.
H. A. Parhusip, S. Trihandaru, A. H. Heriadi, P. P. Santosa, and M. D. Puspasari, “Data Exploration Using Tableau and Principal Component Analysis,” Int. J. Informatics Vis., vol. 6, no. 4, pp. 911–920, 2022, doi: 10.30630/joiv.6.4.952.
P. M. Paithane, “Random Forest Algorithm Use for Crop Recommendation,” J. Eng. Technol. Ind. Appl., vol. 9, no. 43, pp. 34–41, 2023, doi: 10.5935/jetia.v9i43.906.
A. B. Wiratman and Wella, “Personalized Learning Models Using Decision Tree and Random Forest Algorithms in Telecommunication Company,” Int. J. Informatics Vis., vol. 8, no. 1, pp. 318–325, 2024, doi: 10.62527/joiv.8.1.1905.
M. K. A. Rahman et al., “Hand Gesture Recognition Based on Continuous Wave (CW) Radar Using Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) Methods,” Int. J. Informatics Vis., vol. 6, no. 1–2, pp. 188–194, 2022, doi: 10.30630/joiv.6.1-2.926.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dahlan Susilo, Diyah Ruswanti, Supriyanta, Wawan Nugroho
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
