Application of the K-Means Clustering Algorithm for  Classifying High-Achieving Students at MI Irsyadul  Athfal Depok

Ilham Mubarok; Rita Wahyuni Arifin

doi:10.59934/jaiea.v5i1.1714

Authors

Ilham Mubarok Universitas Bina Insani
Rita Wahyuni Arifin Universitas Bina Insani

DOI:

https://doi.org/10.59934/jaiea.v5i1.1714

Keywords:

Education, outstanding student prediction, K-Means clustering algorithm, machine learning, academic and non-academic data.

Abstract

Education is an important foundation in the development of knowledge, skills, and attitudes in accordance with the values of life. This process aims to improve people's standard of living. In this context, Madrasah Ibtidaiyah (MI) Irsyadul Athfal faces a challenge in selecting outstanding students which is still done subjectively, with the main focus on academic scores without considering other aspects. To improve objectivity and efficiency in assessment, this research proposes the use of machine learning technology with the K-Means clustering algorithm. This research aims to develop a prediction model for outstanding students based on academic and non-academic data, such as attendance, summative, mid- and end-of-semester assessments, and extracurricular activities. The K-Means algorithm was chosen because of its advantages in clustering data that is fast, simple, and flexible. The research was conducted at MI Irsyadul Athfal with observation data from local students. The results of the research are expected to provide accurate predictions of outstanding students, support more objective decision making, and increase student motivation in developing their potential. In addition, this research is also a reference for the development of similar prediction systems in other educational institutions.

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