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Suhefi Oktarian
Universitas Putra Indonesia YPTK Padang
Computer Science & IT

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Clustering Students' Interest Determination in School Selection Using the K-Means Clustering Algorithm Method Suhefi Oktarian; Sarjon Defit; Sumijan
Jurnal Informasi dan Teknologi 2020, Vol. 2, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v2i3.65

Abstract

Education is one of the main focuses of the Indragiri Hilir Regency Government work program. Based on data from the Regional Central Statistics Agency of Indragiri district in 2019, the high level of student interest in attending school is at the elementary and junior high school levels. K-means clustering is a data grouping technique by dividing existing data into one or more clusters. School grouping based on student interest is important because at the high school level students' interest in education has decreased so that information is needed which schools are in great demand, sufficient interest and less interest by students at the junior high school level when after finishing elementary school education. This study aims to assist the Education Office in the decision-making process to determine which school students are most interested in as a reference in development both in terms of quality and quantity. The data used in this study is the Dapodikdasmen data in 2019.Data processing in this study uses the K-means clustering method with a total of 3 clusters, namely cluster 0 (C0) is less attractive, Cluster 1 (C1) is quite attractive, cluster 2 (c2) is very interested in students in choosing a school. The results of the clustering process with 2 iterations state that for cluster 0 there are 6 school data, for cluster 1 there are 3 school data, cluster 2 is 1 school data.
Co-Authors Sarjon Defit Sumijan