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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal of Tourism Economics and Policy
HAKIM, Rajul
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Computer Science & IT Economics, Econometrics & Finance Education Public Health Social Sciences Other

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Analysis of Machine Learning Utilization in Identifying Social Assistance Recipients in Aceh Province HAKIM, Rajul; ADNAN, Muhammad; SAFITRI, Winny Dian
Journal of Tourism Economics and Policy Vol. 4 No. 4 (2024): Journal of Tourism Economics and Policy (October - December 2024)
Publisher : PT Keberlanjutan Strategis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38142/jtep.v5i4.1530

Abstract

Poverty is still an ongoing problem in Indonesia, especially in Aceh Province, even though various interventions such as the Program Keluarga Harapan (PKH) and the use of the Kartu Keluarga Sejahtera (KKS) have been implemented. This study aims to classify social assistance recipients more accurately, in order to reduce poverty levels in Aceh Province. This study uses secondary data from the 2023 National Socio-Economic Survey (NSES) with a total of 13,316 household observations and involving 28 independent variables. The results of the study show that the Classification Tree algorithm is able to classify households with an accuracy rate of 80%. The most influential variables in predicting KKS recipients include the education of the head of the household, floor area, number of household members, source of drinking water, and employment status. These findings indicate that a data-driven approach can improve the targeting accuracy of social assistance programs and support poverty alleviation efforts more effectively.
Predicting the Accuracy of Non-Cash Food Assistance Program in Aceh Using Logistic Regression Biner in Aceh Province: How is the Condition? Dian Safitri, Winny; Dian Saditri, Winny; Azzahra, Fina; Hakim, Rajul; Radha Novarianti , Siti
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33969

Abstract

This study examines the effectiveness of the Non-Cash Food Assistance Program (BPNT) in alleviating poverty in Aceh Province, the poorest province on the island of Sumatra. The research utilizes data from the 2022 National Socioeconomic Survey (Susenas) to analyze household characteristics and determine factors influencing BPNT eligibility. Binary logistic regression and data balancing with SMOTE were applied to assess classification accuracy. Results indicate that households without adequate basic amenities, such as proper toilets and electricity, and those with limited access to resources, such as well water, firewood for cooking, and lack of household assets, are more likely to qualify for BPNT. The logistic regression model achieved an accuracy of 80.83%, with high recall for "Recipient" classification. This study findings highlight is that economic hardship, household size, and physical conditions are significant determinants of BPNT eligibility. This study suggests that targeted assistance for poverty alleviation can be optimized through refined eligibility criteria and data accuracy improvements.
Co-Authors Azzahra, Fina Dian Saditri, Winny Muhammad Adnan Radha Novarianti , Siti Winny Dian Safitri