Vol.12, No.1, February 2023.                                                                                                                                                                              ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

House Price Prediction Model Using Random Forest in Surabaya City


Rinabi Tanamal, Nathalia Minoque, Trianggoro Wiradinata, Yosua Soekamto, Theresia Ratih


© 2023 Rinabi Tanamal, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 1, Pages 126-132, ISSN 2217-8309, DOI: 10.18421/TEM121-17, February 2023.


Received: 17 September 2022.

Revised:   25 October 2022.
Accepted:  31 October 2022.
Published: 27 February 2023.




A home is one of many fundamental human needs. Therefore, it is essential to arrange so that each family has a separate dwelling. Several prediction algorithms are presented in this study to forecast future property values. By interviewing real estate agents, combining many interviews with multiple agents engaged in the purchasing and selling of homes. Consequently, this study investigates Surabaya Real estate price forecasting models employing Random Forest machine learning algorithms and adopting seventeen regularly used characteristics from real estate agents, which are the most influential factor in determining house prices. The final model may assist in determining the appropriate price for the house. Several research trials have been conducted to achieve a high predictive value; however, the highest predictive value was achieved by using 80% of the data set for training and 20% of the data set for testing to provide output values with an 88% accuracy rate.


Keywords –housing price prediction, machine learning, classification, random forest, house sales, Surabaya city.



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