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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Sinawati, S
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Implementation of Data Mining Using K-medoids Clustering Method for Determining Social Assistance Recipients Nurzainun, Ratnasari; Sinawati, S; Prayogi, Denis
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.372

Abstract

Regarding social assistance, the village head, “Lurah” of Selumit, is currently reviewing data on residents based on government-provided statistics on the capabilities of low-income families and the need for social assistance.  Therefore, this study proposes K-medoid clustering to ensure that the assistance provided is appropriate. The study collected 62 data on social assistance recipients consisting of 6 criteria, namely employment, assets, income, jak (who are still dependents), home status, and home conditions. K-Medoids analysis using the Euclidean distance function with K=3 produces cluster 1 with 12 data, cluster 2 with 31 data, and cluster 3 with 19 data. The recipients prioritized to receive social assistance are the data in cluster 2 by calculating the average of the most considerable maximum weight value.
Implementation of Data Mining Using K-medoids Clustering Method for Determining Social Assistance Recipients Nurzainun, Ratnasari; Sinawati, S; Prayogi, Denis
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.372

Abstract

Regarding social assistance, the village head, “Lurah” of Selumit, is currently reviewing data on residents based on government-provided statistics on the capabilities of low-income families and the need for social assistance.  Therefore, this study proposes K-medoid clustering to ensure that the assistance provided is appropriate. The study collected 62 data on social assistance recipients consisting of 6 criteria, namely employment, assets, income, jak (who are still dependents), home status, and home conditions. K-Medoids analysis using the Euclidean distance function with K=3 produces cluster 1 with 12 data, cluster 2 with 31 data, and cluster 3 with 19 data. The recipients prioritized to receive social assistance are the data in cluster 2 by calculating the average of the most considerable maximum weight value.
Co-Authors Nurzainun, Ratnasari Prayogi, Denis