DATA MINING STUDY FOR GROUPING ELEMENTARY SCHOOLS IN BOJONEGORO REGENCY BASED ON CAPACITY AND EDUCATIONAL FACILITIES

  • Denny Nurdiansyah Statistics, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri, Indonesia
  • Saniyatus Saidah Statistics, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri, Indonesia
  • Nita Cahyani Statistics, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss2pp1081-1092
Keywords: Education, Data Mining, K-Means, K-Medoids, Random Clustering

Abstract

The implementation of national education must ensure equitable distribution of educational facilities. However, based on data from the Regional Education Balance Sheet (NPD) in 2021, elementary schools in Bojonegoro District still need to meet the criteria for overall equality. It is mainly related to educational capacity and facilities. It is necessary to group elementary schools based on capacity and educational facilities to solve this problem by applying the clustering method. The research aims to conduct a comparative study of three clustering methods to get the best way to be used for clustering elementary schools in Bojonegoro Regency. This study applies three clustering methods, namely K-Means, K-Medoids, and Random Clustering, which are compared to get the best clustering method. The data used is secondary data representing educational capacity and facilities, namely the number of students, teachers, classrooms, and study groups (Rombel) from the Bojonegoro District Education Office. Obtained the resulting comparison of clustering methods with the best way falls on the K-Means method, which forms 5 clusters. It explained that elementary schools with educational capacity and facilities get highly complete 14 schools (cluster_3), complete 236 schools (cluster_2), fairly complete 176 schools (cluster_4), less complete 310 schools (cluster_1), and incomplete 177 schools (cluster_0). The conclusion that comparing Clustering methods obtained grouping of Elementary School data with the best way falls on the K-Means method by getting 5 clusters.

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Published
2023-06-11
How to Cite
[1]
D. Nurdiansyah, S. Saidah, and N. Cahyani, “DATA MINING STUDY FOR GROUPING ELEMENTARY SCHOOLS IN BOJONEGORO REGENCY BASED ON CAPACITY AND EDUCATIONAL FACILITIES”, BAREKENG: J. Math. & App., vol. 17, no. 2, pp. 1081-1092, Jun. 2023.
Issue
Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Denny Nurdiansyah, Saniyatus Saidah, Nita Cahyani

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