Analisis Silhouette Coefficient pada 6 Perhitungan Jarak K-Means Clustering

Authors

  • Rahmatina Hidayati Universitas Merdeka Malang
  • Anis Zubair Universitas Merdeka Malang
  • Aditya Hidayat Pratama Universitas Merdeka Malang
  • Luthfi Indana Universitas Merdeka Malang

DOI:

https://doi.org/10.33633/tc.v20i2.4556

Keywords:

K-Means, Clustering, Silhouette Coefficient

Abstract

Clustering merupakan proses pengelompokan sekumpulan data ke dalam klaster yang memiliki kemiripan. Kemiripan dalam satau klaster ditentukan dengan perhitungan jarak. Untuk melihat perfoma beberapa perhitungan jarak, dalam penelitian ini penulis menguji pada 6 data yang memiliki atribut berbeda, yakni 2, 3, 4, dan 6 atribut. Dari hasil uji perbandingan rumus jarak pada K-Means clustering menggunakan Silhouette coefficient dapat disimpulkan bahwa: 1) Chebyshev distance memiliki performa yang stabil baik untuk data dengan sedikit atribut maupun banyak. 2) Average distance memiliki hasil Silhouette coefficient paling tinggi dibandingkan dengan pengukuran jarak lain untuk data yang memiliki outliers seperti data 3. 3) Mean Character Difference mendapatkan hasil yang baik hanya untuk data dengan sedikit atribut. 4) Euclidean distance, Manhattan distance, dan Minkowski distance menghasilkan nilai baik untuk data yang memiliki sedikt atribut, sedangkan untuk data yang banyak atribut mendapatkan nilai cukup yang mendekati 0,5.

References

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Anggara, M., Sujiani, H. dan Nasution, H., 2016, Pemilihan Distance Measure Pada K-Means Clustering Untuk Pengelompokkan Member Di Alvaro Fitness, Jurnal Sistem dan Teknologi Informasi (JUSTIN), Vol. 1, No. 1, hal 1-6.

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http://covid19.go.id diakses Februari 2021

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Published

2021-05-19

Issue

Vol. 20 No. 2 (2021): Mei 2021

Section

Articles

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Copyright (c) 2021 Rahmatina Hidayati

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