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All Journal INTECOMS: Journal of Information Technology and Computer Science
Akbar Suseno Tri Maulana
Universitas Pamulang
Computer Science & IT

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PERBANDINGAN METODE HASIL EVALUASI PENGELOMPOKAN PENERIMA BANTUAN SOSIAL TUNAI (BST) BERDASARKAN PARAMETER KELAYAKAN BANTUAN SOSIAL MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS (STUDI KASUS: KECAMATAN SOLEAR) Akbar Suseno Tri Maulana; Achmad Lutfi Fuadi
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/v9faqe45

Abstract

Cash Social Assistance (BST) is a government program aimed at supporting economically vulnerable communities. Accurate identification of eligible beneficiaries is essential to ensure effective and targeted assistance. This study compares the performance of the K-Means and K-Medoids clustering algorithms in grouping BST beneficiaries based on socioeconomic eligibility parameters in Solear District. A quantitative data mining approach was employed using a dataset of 2,000 beneficiary records processed with RapidMiner. The research included data preprocessing, transformation, normalization, clustering, and evaluation using the Davies-Bouldin Index (DBI). Three clusters were generated based on eighteen socioeconomic attributes. The results show that the K-Means algorithm achieved a DBI value of -2.245, outperforming K-Medoids with a DBI value of -1.694. The lower DBI value indicates that K-Means produced more optimal cluster quality and better separation among beneficiary groups. These findings suggest that K-Means is more suitable for supporting objective and data-driven classification of BST beneficiaries.
Co-Authors Achmad Lutfi Fuadi