Muhammad Fadhli, Yuwaldy Away, Agussabti Agussabti, Nasaruddin Nasaruddin
In 2023, the poverty rate in Aceh reached 14.45%, significantly higher than the national poverty rate of 9.36 %. One of the indicators of poverty is the quality of housing, specifically the availability of habitable homes for underprivileged communities. According to data from the Taskforce for the Acceleration of Poverty Alleviation of the Aceh Development Planning Board, the number of impoverished households in Aceh amounted to 606,793. To obtain accurate and precise data on uninhabitable households among the poor through classification using supervised learning in machine learning, thereby assisting the Aceh government in reconstructing substandard homes. The classification of uninhabitable homes was carried out using various variables, including decile, percentiles, household size, occupation of the head of the household, marital status, home ownership status, type of roofing, wall materials, flooring type, installed electricity capacity, and eligibility for housing assistance. Machine learning models utilized in this process included the Decision Tree Classifier, Random Forest Classifier, Logistic Regression, and Naïve Bayes Classifier. The methodology consisted of Data Collection, Data Preprocessing, Supervised Learning, and Evaluation Metrics. The results using the Decision Tree Classifier show an accuracy of 99.92 %, precision of 99.81%, recall of 99.74%, and an F1-score of 99.77 %. The Random Forest Classifier achieved an accuracy of 99.92 %, precision of 99.86 %, recall of 99.72 %, and an F1-score of 99.79 %. The Logistic Regression model resulted in an accuracy of 87.27%, precision of 71.59%, recall of 50.55%, and an F1-score of 59.26%. The Naïve Bayes Classifier achieved an accuracy of 87.32 %, precision of 64.71 %, recall of 67.67 %, and an F 1 -score of 66.16 %. © 2025 IEEE.
School of Engineering, Universitas Syiah Kuala, Communication, Informatics, and Encryption Services Office of Aceh Province, Banda Aceh, Indonesia; Universitas Syiah Kuala, Department of Electrical and Computer Engineering, Banda Aceh, Indonesia; Universitas Syiah Kuala, Department of Agriculture, Banda Aceh, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock