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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Journal of ICT Research and Applications Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Indonesian Journal of Information System Teknika Eduvest - Journal of Universal Studies ABDIMASTEK
Joko Siswantoro
Departement Of Informatics Engineering, Faculty Of Engineering, Universitas Surabaya, Jalan Raya Kali Rungkut, Surabaya, 60293
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Social Sciences

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Journal : journal of information technology and computer science

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Machine Learning Comparison for Housing Rehabilitation Beneficiary Selection in Surabaya Christy, Cathrine Abigael; Harida Budianti, Luluk; Hary Prasetyo, Daniel; Siswantoro, Joko
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112908

Abstract

The Surabaya City Government launched the ‘Dandan Omah’ Social Rehabilitation Program for Uninhabitable Houses (RTLH) in 2022. However, beneficiary selection for the 2022–2024 period was conducted manually, which may have led to subjective and less targeted decisions. This study proposed a data-driven decision support approach by comparing Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) across 2, 4, and 5 classification tasks. A dataset of 1700 records comprising 11 features was evaluated using an 80:20 train–test split and  10-fold cross-validation. RF consistently achieved the best performance across all scenarios while SVM demonstrated competitive baseline performance. After tuning, DT surpassed SVM on 5-class tasks and matched RF on 2-class tasks, making optimized DT a viable interpretable alternative when decision accountability was essential. KNN consistently underperformed due to the curse of dimensionality. Overall performance declined as classification complexity increased, attributed to variability in field assessment standards. These findings suggested that RF and optimized DT could support more standardized and objective RTLH beneficiary selection. Future studies employing RF were recommended to identify influential input features to optimize model performance while maintaining explainability. Re-standardization of field assessment protocols was also recommended to improve data quality.
Co-Authors Anasthasya Averina Anton Satria Prabuwono Anton Satria Prabuwono Arwoko, Heru Asmawti, Endah Azizi Abdullah Azizi Abdullah Bahari Indrus Christy, Cathrine Abigael Edwin Indarto Harida Budianti, Luluk Hary Prasetyo, Daniel Helen Hadi Januar Rahmadiarto Jessica Millenia Mohammad Farid Naufal Mohammad Farid Naufal Mohammad Farid Naufal, Mohammad Farid Monica Widiasri, Monica Muhammad Alifian Fajar Pratama Muhammad Ghifari Kusuma Wicaksono Soebroto, Juan Timothy Sofia Ariyani Vincentius Riandaru Prasetyo Wirya Wonggo