Comparative Analysis of Artificial Neural Network (ANN) and Random Forest in House Price Prediction
Abstract
With the advancement of information technology, the application of machine learning in the property industry, particularly for house price prediction, has become increasingly important. Technology plays a crucial role in speeding up and enhancing the accuracy of property buying and selling processes. Therefore, the role of machine learning technology can be utilized to meet the need for improving the accuracy of house price predictions in major cities of developing countries, such as Bandung. This research aims to analyze the effectiveness of the Artificial Neural Network and Random Forest algorithms in predicting house prices in Bandung. The data used includes house sales data in Bandung, covering land area, building area, number of bedrooms, number of bathrooms, number of parking spaces, and the subdistrict location. The analysis of the algorithms is conducted by comparing the performance testing results of both algorithms using performance metrics for regression models such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-Square (R2). Additionally, this research analyzes which data ratio among the training, validation, and test data yields the best results. The research findings indicate that the model with a data ratio of 60:20:20 produces the best performance for both algorithms. The Random Forest algorithm demonstrates superior performance with results of MAE: 0.0470; MSE: 0.0079; RMSE: 0.0888; and R2: 0.7085.
Keywords
References
C. Kurniawan, L. C. Dewi, W. Maulatsih, and W. Gunadi, “Factors Influencing Housing Purchase Decisions of Millennial Generation in Indonesia,” International Journal of Management (IJM), vol. 11, no. 4, pp. 350–365, 2020, [Online]. Available: http://www.iaeme.com/IJM/index.asp350http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=4JournalImpactFactor
F. Zulkifli and H. Ismail, “FACTORS INFLUENCING HOUSE BUYER’S DECISION IN MALAYSIA. CASE STUDY: SEPANG, SELANGOR,” 2023.
U. Musa, W. Zahari, and W. Yusoff, “The Influence of Housing Components on Prices of Residential Houses: A Review of Literature,” 2015.
P. Assudani and C. Wankhede, “Analysing the Factors Influencing the House Prices and Studying House Price Prediction Methods,” International Journal of Next-Generation Computing, Nov. 2022, doi: 10.47164/IJNGC.V13I5.952.
A. Kaushal and A. Shankar, “House Price Prediction Using Multiple Linear Regression,” 2021. [Online]. Available: https://ssrn.com/abstract=3833734
W. Weng, “Research on the House Price Forecast Based on machine learning algorithm,” 2022.
P. Meghana et al., “Analysis of Neural Network Algorithm in Comparison to Multiple Linear Regression and Random Forest Algorithm,” in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 437–443. doi: 10.1109/ICETSIS61505.2024.10459496.
M. Rahman and M. Asadujjaman, “Implementation of Artificial Neural Network on Regression Analysis,” 2021 5th Annual Systems Modelling Conference, SMC 2021, Sep. 2021, doi: 10.1109/SMC53803.2021.9569881.
J. Gao, K. Wang, X. Kang, H. Li, and S. Chen, “Ultra-short-term electricity load forecasting based on improved random forest algorithm,” AIP Adv, vol. 13, no. 6, Jun. 2023, doi: 10.1063/5.0153550.
Q. Truong, M. Nguyen, H. Dang, and B. Mei, “Housing Price Prediction via Improved Machine Learning Techniques,” Procedia Comput Sci, vol. 174, pp. 433–442, 2020, doi: 10.1016/j.procs.2020.06.111.
L. Harris and M. Grzes, “Comparing Explanations between Random Forests and Artificial Neural Networks,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, pp. 2978–2985. doi: 10.1109/SMC.2019.8914321.
M. Geerts, S. vanden Broucke, and J. De Weerdt, “A Survey of Methods and Input Data Types for House Price Prediction,” May 01, 2023, MDPI. doi: 10.3390/ijgi12050200.
N. Li and R. Y. M. Li, “A bibliometric analysis of six decades of academic research on housing prices,” International Journal of Housing Markets and Analysis, vol. 17, no. 2, pp. 307–328, Feb. 2024, doi: 10.1108/IJHMA-05-2022-0080.
N. F. Savitri and N. Nasrudin, “PERAMALAN INDEKS HARGA PROPERTI RESIDENSIAL DI KOTA BANDUNG TAHUN 2023,” Jurnal Kebijakan Pembangunan Daerah, vol. 7, no. 2, pp. 140–157, Nov. 2023, doi: 10.56945/jkpd.v7i2.249.
S. Shanmuganathan, “Studies in Computational Intelligence 628 Artificial Neural Network Modelling,” 2016. [Online]. Available: http://www.springer.com/series/7092
Z. Zhang, “Artificial Neural Network,” Multivariate Time Series Analysis in Climate and Environmental Research, pp. 1–35, 2018, doi: 10.1007/978-3-319-67340-0_1.
X. Xu and Y. Zhang, “House price forecasting with neural networks,” Intelligent Systems with Applications, vol. 12, p. 52, 2021, doi: 10.1016/j.iswa.2021.20.
G. Biau and E. Scornet, “A Random Forest Guided Tour,” Journal TEST, vol. 25, pp. 197–227, 2016, [Online]. Available: http://www.kaggle.com/c/dsg-hackathon
V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines,” Ore Geol Rev, vol. 71, pp. 804–818, Aug. 2015, doi: 10.1016/j.oregeorev.2015.01.001.
A. Fatima, N. Nazir, and M. G. Khan, “Data Cleaning In Data Warehouse: A Survey of Data Pre-processing Techniques and Tools,” International Journal of Information Technology and Computer Science, vol. 9, no. 3, pp. 50–61, Mar. 2017, doi: 10.5815/ijitcs.2017.03.06.
J. Tang et al., “Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage,” BMC Med Inform Decis Mak, vol. 22, no. 1, Dec. 2022, doi: 10.1186/s12911-022-02018-x.
J. D. Karch, “OUTLIERS MAY NOT BE AUTOMATICALLY REMOVED 1 Outliers May Not Be Automatically Removed,” 2022. [Online]. Available: https://orcid.org/0000-0002-1625-2822.
M. R. Firmansyah, “Stroke Classification Comparison with KNN through Standardization and Normalization Techniques,” Advance Sustainable Science, Engineering and Technology, vol. 6, no. 1, p. 02401012, Jan. 2024, doi: 10.26877/asset.v6i1.17685.
J. Bilmes, “Underfitting and Overfitting in Machine Learning,” 2020.
F. E. Rahayuningtyas, F. N. Rahayu, Y. Azhar, and I. Artikel, “Prediksi Harga Rumah Menggunakan General Regression Neural Network,” JURNAL INFORMATIKA, vol. 8, no. 1, 2021, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Real
A. Saiful, S. Andryana, and A. Gunaryati, “Prediksi Harga Rumah Menggunakan Web Scrapping Dan Machine Learning Dengan Algoritma Linear Regression,” Jurnal Teknik Informatika dan Sistem Informasi , vol. 8, no. 1, 2021, [Online]. Available: http://jurnal.mdp.ac.id
M. Muneeb, “LSTM input timestep optimization using simulated annealing for wind power predictions,” PLoS One, vol. 17, no. 10 October, Oct. 2022, doi: 10.1371/journal.pone.0275649.
A. N. Calugar, W. Meng, and H. Zhang, “Towards Artificial Neural Network Based Intrusion Detection with Enhanced Hyperparameter Tuning,” 2022.
K. Al Faaath, “Daftar Harga Rumah di Kota Bandung.” Accessed: Jun. 08, 2024. [Online]. Available: https://www.kaggle.com/datasets/khaleeel347/harga-rumah-seluruh-kecamatan-di-kota-bandung
T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Jul. 19, 2022, Copernicus GmbH. doi: 10.5194/gmd-15-5481-2022.
S. Sharma, V. Jhaketiya, A. Kaul, A. A. Raza, S. Ahmed, and Mohd. Naseem, “Automatic Prediction of Road Angles using Deep Learning-Based Transfer Learning Models,” IOP Conf Ser Mater Sci Eng, vol. 1099, no. 1, p. 012060, Mar. 2021, doi: 10.1088/1757-899x/1099/1/012060.
V. Plevris et al., “INVESTIGATION OF PERFORMANCE METRICS IN REGRESSION ANALYSIS AND MACHINE LEARNING-BASED PREDICTION MODELS,” in The 8th European Congress on Computational Methods in Applied Sciences and Engineering, 2022.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci Model Dev, vol. 7, no. 3, pp. 1247–1250, Jun. 2014, doi: 10.5194/gmd-7-1247-2014.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
M. Li, M. Soltanolkotabi, and S. Oymak, “Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks,” in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, S. Chiappa and R. Calandra, Eds., in Proceedings of Machine Learning Research, vol. 108. PMLR, Jul. 2020, pp. 4313–4324. [Online]. Available: https://proceedings.mlr.press/v108/li20j.html
P. R. Togatorop, M. Sianturi, D. Simamora, and D. Silaen, “Optimizing Random Forest using Genetic Algorithm for Heart Disease Classification,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, p. 60, Aug. 2022, doi: 10.24843/lkjiti.2022.v13.i01.p06.
DOI: https://doi.org/10.17509/seict.v5i2.75945
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Journal of Software Engineering, Information and Communication Technology (SEICT)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Software Engineering, Information and Communicaton Technology (SEICT),
(e-ISSN:2774-1699 | p-ISSN:2744-1656) published by Program Studi Rekayasa Perangkat Lunak, Kampus UPI di Cibiru.
Indexed by.
