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All Journal International Journal of Applied Mathematics and Computing.
Antonieta Aryuka Paskalia Nggotu
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Computer Science & IT Mathematics

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Identification of Housing Eligibility Status Using Family Data in Samarinda City Antonieta Aryuka Paskalia Nggotu; Hamdani, Hamdani; Anindita Septiarini
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.294

Abstract

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.
Co-Authors Anindita Septiarini, Anindita Hamdani Hamdani