PENERAPAN METODE K-MEANS CLUSTERING UNTUK PENGELOMPOKAN SISWA BERDASARKAN PRESTASI DI SD N 03 SANGGANG SUKOHARJO

Authors

  • Dika Restyanto Universitas Tiga Serangkai, Indonesia
  • Kumaratih Sandradewi
  • Retno Tri Vulandari Universitas Tiga Serangkai, Indonesia

DOI:

https://doi.org/10.30646/tikomsin.v13i2.1007

Keywords:

Data Mining, K-Means Clustering, Student Achievement Grouping, Educational Data Analysis, Modified Partition Coefficient

Abstract

The rapid advancement of data science has made data processing a critical requirement across various fields, including education. Educational institutions are increasingly required to leverage available resources and information systems to enhance competitiveness and support strategic decision-making. Student achievement is generally assessed through both theoretical and practical subjects; however, determining achievement groups (very good, good, sufficient) often lacks efficiency, limiting early identification by homeroom teachers. This study applies the K-Means Clustering method to classify students' achievement levels at SD N Sanggang 03. The objective is to develop a system that assists teachers in grouping students based on performance categories—very good, good, and sufficient—thereby supporting data-driven decision-making in academic evaluation. The system was implemented using PHP and MySQL, with evaluation employing the Modified Partition Coefficient to measure clustering quality.

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Published

2025-10-31

Issue

Vol. 13 No. 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025

Section

Articles

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