Implementasi K-Means pada Klasterisasi Data Intervensi Prioritas Penerima Bantuan Sosial

Ari Kurniawan Saputra, Erlangga Erlangga, Taqwan Thamrin

Abstract


Implementation of K-Means in Clustering Data for Priority Social Assistance Recipients. There are various types of social assistance financed through the State Budget (APBN), such as Non-Cash Food Assistance (BPNT), Family Hope Program (PKH), Cash Social Assistance (BST), Micro Business Productive Assistance (BPUM), Set Top Box (STB) Assistance, Basic Food Assistance, Contribution Assistance Recipients (PBI), Pre-Employment Assistance, and People's Business Credit Assistance (KUR). South Lampung Regency is currently ranked third with 145.85 thousand poor people. The need for clustering data on types of social assistance is an effort by the government to intervene in the priority of recipients of Social Assistance (Bansos) to be right on target. K-means is a method used to group similar and correlated objects in a non-hierarchical clustering. This research aims to test the accuracy of K-Means algorithm data clustering. The results of testing the implementation of the K-Means algorithm in clustering data on priority interventions for social assistance recipients obtained the closest distance value to the Centroid, namely C1 = 768, C2 = 1152, and C3 = 192. The findings of this study indicate that the implementation of the K-Means algorithm in data clustering is quite accurate and is expected to be used as a solution for priority interventions for social assistance recipients to be right on target.

Keywords


Clustering; K-Means; Social Assistance; Bantuan Sosial; K-Means; Klasterisasi

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DOI: http://dx.doi.org/10.36448/expert.v14i1.3717

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