Industry Class Clustering of Software Expertise Competency at SMKN 2 Kraksaan Using Constrained K-Means Clustering Algorithm

Matlubul Khairi; M.  Syafiih; Ahmad  Khairi

doi:10.47709/cnahpc.v6i3.4214

Authors

Matlubul Khairi Universitas Nurul Jadid
M. Syafiih Universitas Nurul Jadid
Ahmad Khairi Universitas Nurul Jadid

DOI:

10.47709/cnahpc.v6i3.4214

Keywords:

Constrained K-Means Clustering, Linear Programming Algorithm, Academic Proficiency Test, Software Engineering Competency

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Abstract

Addressing the gap between school education and industry needs is a recurring concern, as many graduates struggle to enter the workforce due to lacking practical skills. Industry Classes aim to bridge this gap by preparing students with relevant skills and knowledge aligned with real-world industry demands. This study focuses on the application of Constrained K-Means Clustering to categorize students in the software engineering competency classes at SMKN 2 Kraksaan. This algorithm modifies traditional K-Means by utilizing Linear Programming Algorithm (LPA), ensuring each cluster meets predefined subject requirements. The research involves analyzing academic proficiency test data (TKDA) from 96 X-grade students, evaluating their abilities in analogy, series, figural, mathematical, and recall skills. Using a 3-cluster approach, each with 32 to 60 student capacity constraints, the study aims to optimize student distribution for effective learning outcomes. Evaluation through silhouette method yielded a score of 0.3199, indicating satisfactory separation between clusters with overlap to address. Cluster analysis revealed Cluster 2 as the most proficient, showcasing strengths in recall and series attributes critical for software engineering. These findings suggest that Constrained K-Means Clustering is effective in classifying students, highlighting Cluster 2 as optimal for software engineering competencies at SMKN 2 Kraksaan. Future research should focus on enhancing data quality, expanding sample size, and refining algorithms for improved clustering accuracy and effectiveness.

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