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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal Ilmiah Mahasiswa Jurnal Kridatama Sains dan Teknologi SOLUSI Jurnal Disastri: Pendidikan Bahasa dan Sastra Indonesia Dedikasi: Jurnal Pengabdian Kepada Masyarakat BERBAKTI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Multidisiplin Ilmu COSMOS Jejak digital: Jurnal Ilmiah Multidisiplin Journal of Applied Holistic Nursing Science
Ana Fauziah
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Social Sciences Other

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PERBANDINGAN METODE K-MEANS DAN DBSCAN PADA ANALAISIS KLASTER MULTIVARIAT PROFIL SISWA Ana Fauziah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 3 No. 1 (2026): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/1kays705

Abstract

In educational practice, students are often treated as a homogeneous group within a heterogeneous environment. The "one-size-fits-all" approach has proven ineffective as it overlooks the segmented needs of diverse individuals. This study aims to perform student profiling through cluster analysis to identify groups requiring specialized support or enrichment. The methodology compares two clustering algorithms: K-Means (partition-based) and DBSCAN (density-based). Prior to modeling, optimal parameters were determined using the Elbow method for K-Means and the K-distance graph for DBSCAN. Model evaluation was based on the number of resulting clusters and the Silhouette Score. The results indicate that DBSCAN outperformed K-Means with a Silhouette Score of 0.4 compared to 0.3. Furthermore, DBSCAN produced a more optimal and cohesive structure with 5 clusters, making it more interpretable for student profiling than the 8-cluster solution from K-Means. The analysis successfully identified five student profiles: High Achievers, Above Average, Moderate, Below Average, and At-Risk/Underprivileged. This study concludes that multivariate cluster analysis is an effective instrument for targeted educational intervention, particularly in prioritizing economic aid and academic mentoring for vulnerable groups without relying on demographic identity as a determinant of individual ability.
Co-Authors Ahya, Akhmad Sauqi Budiyono Budiyono Damayanti, Elok Heny Sulistyowati Herlina Hermanto Hermanto Mursid Raharjo Natasya Ingga Felani Fauzi Nugroho, Dwi Widi Pratito Sri Sofiarini, Andriana Sukmarini, Mita Akbar Wardana, Edy Surya Yolanda, Yaspin