House Price Prediction in Surabaya Using Backpropagation Neural Network
Abstract
This research develops a house price prediction system in Surabaya using the Backpropagation Neural Network (BPNN) method. The dataset was obtained through web scraping of property listings, resulting in 3,435 records with 52 attributes. To improve stability, the target variable (house price) was transformed using natural logarithms. Several neural network architectures were tested, and the best configuration [32, 64, 32] achieved Mean Absolute Error (MAE) of 0.3125, Root Mean Squared Error (RMSE) of 0.4201, R² of 0.7138, and Mean Absolute Percentage Error (MAPE) of 1.46%. A multi-run evaluation of 20 iterations confirmed consistency of results. The model was implemented as a web-based application using Flask, allowing users to predict house prices in real-time. This research shows that BPNN is reliable for property price forecasting and can support decision-making in the housing market.
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References
D. Geltner, N. G. Miller, J. Clayton, and P. Eichholtz, Commercial Real Estate Analysis and Investments, 3rd ed. Mason, OH: Cengage Learning, 2013.
Bank Indonesia, “Survei Harga Properti Residensial (SHPR),” Jakarta, 2024.
Badan Pusat Statistik (BPS), “Statistik Perumahan dan Permukiman Indonesia,” 2023.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.A
S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ: Pearson, 2009.A
X. Zhang, Y. Li, and H. Wang, “Prediction of housing prices based on neural networks,” Procedia Computer Science, vol. 199, pp. 883–890, 2022.A
A. Khashei and M. Bijari, “An artificial neural network (p,d,q) model for time series forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479–489, 2010.
J. Kok, A. L. Polak, and S. C. Wong, “Machine learning for house price prediction,” Computers, Environment and Urban Systems, vol. 91, p. 101710, 2022.
S. B. Taşpınar and Y. Yildiz, “Comparison of machine learning algorithms for house price prediction,” Sustainability, vol. 13, no. 11, p. 6232, 2021.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. San Francisco, CA: Morgan Kaufmann, 2012.
G. Shmueli, N. R. Patel, and P. C. Bruce, Data Mining for Business Intelligence. Hoboken, NJ: Wiley, 2007.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springer, 2009.
M. Mitchell, Web Scraping with Python, 2nd ed. Sebastopol, CA: O’Reilly Media, 2018.
P. C. Austin and J. V. Tu, “Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality,” J. Clin. Epidemiol., vol. 57, no. 11, pp. 1138–1146, 2004.
Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: Chapman & Hall/CRC, 2012.
H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433–459, 2010.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural Networks, vol. 4, no. 2, pp. 251–257, 1991.
F. Chollet, Deep Learning with Python, 2nd ed. Shelter Island, NY: Manning, 2021.
M. Grinberg, Flask Web Development: Developing Web Applications with Python, 2nd ed. Sebastopol, CA: O’Reilly Media, 2018.
A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. Sebastopol, CA: O’Reilly Media, 2023.
T. H. Davenport and D. Patil, “Data scientist: The sexiest job of the 21st century,” Harvard Business Review, vol. 90, no. 10, pp. 70–76, 2012.
P. Domingos, “A few useful things to know about machine learning,” Communications of the ACM, vol. 55, no. 10, pp. 78–87, 2012.
C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006.
B. Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Hoboken, NJ: Wiley, 2016.
Copyright (c) 2025 Dimas Fajri Pamungkas, Abdul Rezha Efrat Najaf, Reisa Permatasari
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