Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi
Abstract
Algoritma Partitioning Around Medoid dikenal dengan K-medoids. Algoritma K-medoids lebih cocok digunakan pada Dataset yang memiliki outlier. Karena K-medoids merupakan perbaikan dari algoritma K-means pada clustering yang kurang baik dalam menangani dataset yang memiliki outliers. Algoritma K-medoids menentukan pusat cluster berdasarkan perwakilan objek cluster yang disebut dengan medoid. Medoid adalah objek cluster yang terletak paling sentral, dengan jumlah jarak minimum ke titik lain. Untuk menutupi kelemahan metode K-medoids dalam menentukan jumlah k awal secara random digunakan metode elbow. Evaluasi kelayakan algoritma K-medoids dalam pembentukan klasterisasi dilakukan pengukuran terhadap silhouette coefficient dan Davies-Bouldin index.
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