Analisis Performa Clustering: K-Means dan Similarity Matrix dalam Evaluasi Silhouette, DBI, CHI, dan Dunn Index

Authors

  • Bhima Fajar Ramadhan Teknik Informatika, Universitas Pamulang , Indonesia
  • Bimo Musthafa Abdillah Teknik Informatika, Universitas Pamulang , Indonesia
  • Miftahul Hidayatullah Teknik Informatika, Universitas Pamulang , Indonesia
  • Muhamad Nur Faizal Teknik Informatika, Universitas Pamulang , Indonesia
  • Rio Aditya Winata Teknik Informatika, Universitas Pamulang , Indonesia
  • Zurnan Alfian Teknik Informatika, Universitas Pamulang , Indonesia

Keywords:

Clustering, K-Means, Similarity Matrix, Silhouette, DBI, CHI, Dunn Index

Abstract

Clustering merupakan teknik penting dalam data mining yang bertujuan untuk mengelo­­­­­­­mpokkan data berdasarkan kemiripan antar objek. Penelitian ini membahas analisis performa dua pendekatan clustering, yaitu K-Means dan Similarity Matrix, dalam konteks evaluasi kualitas cluster. Pendekatan Similarity Matrix diterapkan menggunakan hierarchical clustering dengan metode complete-linkage, sedangkan K-Means menggunakan data fitur numerik secara langsung. Keduanya diuji pada beberapa dataset dan dievaluasi menggunakan metrik kuantitatif seperti Silhouette Score, Davies-Bouldin Index (DBI), Calinski-Harabasz Index (CHI), dan Dunn Index. Hasil eksperimen menunjukkan bahwa pendekatan K-Means cenderung unggul dalam pemisahan cluster (Silhouette dan CHI lebih tinggi), sedangkan pendekatan Similarity Matrix lebih baik dalam kepadatan dan keseragaman cluster (DBI dan Dunn Index lebih rendah). Temuan ini menegaskan pentingnya pemilihan metode clustering yang sesuai dengan karakteristik data dan tujuan analisis.

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Published

17-07-2025

How to Cite

Ramadhan, B. F., Abdillah, B. M., Hidayatullah, M., Faizal, M. N., Winata, R. A., & Alfian, Z. (2025). Analisis Performa Clustering: K-Means dan Similarity Matrix dalam Evaluasi Silhouette, DBI, CHI, dan Dunn Index. Jurnal Pendidikan Tambusai, 9(2), 22961–22967. Retrieved from https://jptam.org/index.php/jptam/article/view/30265

Issue

Vol. 9 No. 2 (2025): Agustus

Section

Articles of Research

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