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All Journal INFORMATIKA
Muchamad Risqi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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PERBANDINGAN ALGORITMA K-PROTOTYPES DENGAN AGGLOMERATIVE CLUSTERING DALAM SEGMENTASI SISWA BERDASARKAN FAKTOR AKADEMIK DAN SOSIAL Muchamad Risqi; , Muhammad Nashif Farid; , Mohammad Idhom; Trimono Trimono
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/spkmfd39

Abstract

Student performance is affected by internal and external factors such as study time, absenteeism, tutoring, and parental support—factors often overlooked by traditional education methods. This study applies K-Prototypes and Agglomerative Clustering with Gower Distance to segment students using mixed-type data. Five key variables were analyzed: study time, absences, GPA, tutoring, and parental support. The Elbow Method was used to identify the optimal number of clusters, and Silhouette Score to evaluate performance. Results show K-Prototypes outperformed Agglomerative Clustering (0.332 vs 0.186). Three student segments emerged: active students with average GPA, low-risk learners, and high-achievers with minimal external support. These findings can inform more adaptive and data-driven academic interventions for education stakeholders.
Co-Authors , Mohammad Idhom , Muhammad Nashif Farid Trimono Trimono