PERBANDINGAN ALGORITMA K-PROTOTYPES DENGAN AGGLOMERATIVE CLUSTERING DALAM SEGMENTASI SISWA BERDASARKAN FAKTOR AKADEMIK DAN SOSIAL
DOI:
https://doi.org/10.51903/spkmfd39Keywords:
Segmentasi Siswa, K-Prototypes, Gower DistanceAbstract
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.
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