Application of K-Means Clustering on School Identification in the Distribution of Assistance Funds for DPRD Members: Case Study in North Padang Lawas DPRD

Eka Hayana Hasibuan; Aripin Rambe; Dinur Syahputra

doi:10.25008/bcsee.v3i2.1163

Authors

Eka Hayana Hasibuan Informatics Study Program, Faculty of Technology, Battuta University
Aripin Rambe Information Technology Study Program, Faculty of Technology, Battuta University
Dinur Syahputra Informatics Study Program, Faculty of Technology, Battuta University

DOI:

https://doi.org/10.25008/bcsee.v3i2.1163

Keywords:

Distribution DPRD School, Software RapidMiner, Clustering, K-Means Clustering, unsupervised learning

Abstract

In this study, the k-means algorithm was used to group schools and categorize DPRD grants into very feasible, feasible, and impractical categories for better focus. Based on the results of computational analysis using the K-Means clustering algorithm using the Euclidean distance equation for the distribution of DPRD suction subsidies from 52 schools, 28 schools are in the very decent category and 11 schools are decent. In that category, 13 schools were found with fewer categories. executable category. RapidMiner Studio v.7.6 software can group schools based on the distribution needs of DPRD suction tools for more effective and efficient results.

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