CRITERIA ANALYSIS OF COURSE PARTICIPANTS USING K-MEANS: A CASE STUDY OF INET PALEMBANG

Muhammad Rasuandi Akbar; Rezania  Agramanisti Azdy; Yesi  Novaria Kunang; Nurul  Adha Oktarini Saputri

doi:10.33330/jurteksi.v11i4.4112

Muhammad Rasuandi Akbar Universitas Bina Darma
Rezania Agramanisti Azdy Bina Darma University
Yesi Novaria Kunang Bina Darma University
Nurul Adha Oktarini Saputri Bina Darma University

DOI: https://doi.org/10.33330/jurteksi.v11i4.4112

Keywords: clustering, data mining, elbow method, k-means, computer course

Abstract

Abstract: INET Computer Palembang, as a computer training institution, faces difficulties in understanding participant characteristics due to variations in age, educational background, and chosen course packages. This study aims to analyze participant criteria and group them based on similarities using the K-Means Clustering algorithm. The data used were historical records of course participants from 2022 to 2025. The research process followed the CRISP-DM stages, starting from data cleaning and transformation, determining the optimal number of clusters using the Elbow Method, to evaluating cluster quality with the Davies-Bouldin Index. The implementation was carried out using Python and the scikit-learn library. The results show that the optimal number of clusters is k=5 with a Sum of Squared Errors (SSE) value of 1064.66 and a Davies-Bouldin Index (DBI) score of 0.820, indicating good cluster quality. The resulting clustering provides a structured profile of participants and demonstrates that K-Means is effective in segmenting course participants. These findings are expected to assist the institution in designing more targeted training programs.

Keywords: clustering; data mining; elbow method; k-means; computer course

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