Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

Authors

  • I NYM Yoga Saputra Telkom University
  • Siti Saadah Telkom University
  • Prasti Eko Yunanto Telkom University

DOI:

https://doi.org/10.26555/jiteki.v7i2.20997

Keywords:

Backpropagation, Multiple Regression Forecasting, Prediction, Predicted Apartment Prices, Random Forest Forecasting

Abstract

This study focuses on predicting the apartment price index in Indonesia using property survey data from Bank Indonesia. In the era of the Covid-19 pandemic, accurately predicting the sale and purchase price of apartments is essential to minimize the impact of losses, thus making apartment prices attractive to predict. The machine learning approach used to predict the apartment price index are the Random Forest method, the Multiple Regression method, and the Backpropagation method. This study aims to determine which method is more effective in predicting small amounts of data accuracy. The data used is apartment price index data from 2012 to 2019 in the JABODEBEK area. The research will produce prediction accuracy that will determine the effectiveness of the application of the method. The Random Forest method with parameters n_estimators=100 and max_features=â€log2†produces an R2 accuracy of 0.977. The Multiple Regression method with a correlation between the selling price and rental price variables is 0.746, and the rental inflation variable is 0.042 produces an R2 accuracy of 0.559. The Backpropagation method with a 1000-4000-1 hidden scheme and 20000 iterations produces an R2 accuracy of 0.996. Therefore, the Backpropagation method is more suitable in this study compared to the other two methods. The Backpropagation method is suitable because it gets almost perfect accuracy, so this method will minimize losses in investing in buying and selling apartments in the Covid-19 pandemic era.

Author Biographies

I NYM Yoga Saputra, Telkom University

I NYM Yoga Saputra is currently pursuing an undergraduate program at Telkom University, Bandung. He majored in Informatics. His research interests include machine learning, prediction and Intelligent Systems.

Siti Saadah, Telkom University

Siti Saadah received the Bachelor and Master degree in Informatics Engineering from Telkom Institute of Technology (now Telkom University), Bandung, Indonesia in 2009 and 2012. Since 2009, she joined Telkom University as a lecturer in School of Computing. She is Teaching Design and Analysis Algorithm, Artificial Intelligence, Theory Authomata at Telkom University. Her research interests include machine learning, financial computing, AI healthcare, prediction and simulation. Scopus ID: 55523371300, Researcher ID: AAD-6187-2021, Publon ID: 4215578.

Prasti Eko Yunanto, Telkom University

Prasti Eko Yunanto received the B.Sc. on Informatics Engineering from Telkom Institute of Technology (now Telkom University), Bandung, Indonesia in 2012, the M.Sc. on Computing from Telkom University, Bandung, in 2015. Since 2019, he joined Telkom University as a lecturer in School of Computing. His research interests include Biometrics Security and Intelligent System. Scopus ID: 57193832286, Researcher ID: AAD-6421-2021, Publon ID: 4216090,  Orcid: https://orcid.org/0000-0003-1967-9749.

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Published

2021-07-20

How to Cite

[1]
I. N. Y. Saputra, S. Saadah, and P. E. Yunanto, “Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 7, no. 2, pp. 238–248, Jul. 2021.

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Vol. 7 No. 2 (2021): August

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