Property Category Prediction Model using Random Forest Classifier to Improve Property Industry in Surabaya

DOI

10.2991/978-94-6463-144-9_24How to use a DOI?

Keywords

Classification; Data Science; Predictions; Property Industry; Random Forest; Sustainable City

Abstract

Urban planning is done not only to regulate residential areas, offices, retail spaces, and green spaces but also to ensure that people (community) who live in cities have a decent quality of life. Surabaya is a city that was built in the beginning of Indonesian civilization, so the arrangement of the city of Surabaya is a bit difficult and has an impact on housing costs. In reality, housing development is influenced by businesses in the residential development sector. This causes uneven house types to be built in accordance with the expectations of the government, which could impact the sustainability of Surabaya. This study is crucial because, from the data of Bank Indonesia, in supply and demand index for the property sector in Surabaya has not increased since 2019. Although property price has decreased since the fourth quarter of 2019 because of the Covid 19 pandemic, the demand index has not increased that well. This study intends to assist the process of classifying house types, so the government can make a selection on the house that will be built by the developer. 14 input attributes and 490 data from Surabaya property agencies were used in this study. In this study, random forest is used as the classification technique. The result of the classification model obtained an accuracy value of 89% and F1 score of 89%. A classification prediction model that can be used to determine property classification was found through this study.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yosua Setyawan Soekamto
AU  - Michelle Chandra
AU  - Trianggoro Wiradinata
AU  - Rinabi Tanamal
AU  - Theresia Ratih Dewi Saputri
PY  - 2023
DA  - 2023/05/15
TI  - Property Category Prediction Model using Random Forest Classifier to Improve Property Industry in Surabaya
BT  - Proceedings of the Business Innovation and Engineering Conference (BIEC 2022)
PB  - Atlantis Press
SP  - 256
EP  - 265
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-144-9_24
DO  - 10.2991/978-94-6463-144-9_24
ID  - Soekamto2023
ER  -