PENGUKURAN SILHOUETTE SCORE DAN DAVIES-BOULDIN INDEX PADA HASIL CLUSTER K-MEANS DAN DBSCAN

Yasir Hasan

Abstract


Evaluasi hasil Clustering merupakan langkah kritis dalam analisis data tanpa supervisi. Algoritma Clustering seperti K-Means dan DBSCAN sering digunakan, tetapi memilih algoritma yang tepat untuk suatu dataset bisa menjadi tantangan. Penerapan Silhouette Score dan Davies-Bouldin Index sebagai metrik evaluasi internal untuk mengevaluasi hasil Clustering dari K-Means dan DBSCAN. Metode K-Means dan DBSCAN dipilih karena popularitas dan kemampuannya dalam menangani berbagai jenis data. Silhouette Score memberikan ukuran seberapa baik setiap titik data ditempatkan dalam Cluster mereka sendiri dibandingkan dengan Cluster lain, sementara Davies-Bouldin Index mengevaluasi seberapa jauh Cluster tersebut berada dari yang lain. Penelitian ini dilakukan dengan mengimplementasikan kedua algoritma pada dataset percobaan yaitu dataset kinerja karyawan dan membandingkan hasil evaluasi menggunakan kedua metrik tersebut. Hasil eksperimen kedua metrik evaluasi memberikan wawasan yang berguna dalam mengevaluasi kualitas Clustering kinerja karyawan dari K-Means dan DBSCAN. Dengan demikian, penggunaan Silhouette Score dan Davies-Bouldin Index dapat menjadi panduan yang efektif dalam memilih algoritma Clustering yang sesuai untuk suatu dataset tanpa kebutuhan akan label ground truth.

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DOI: http://dx.doi.org/10.23960/jitet.v12i3S1.5001

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