ANALISIS CLUSTER OTOMATIS MENGGUNAKAN ALGORITMA NOVEL MODIFIED DIFFERENTIAL EVOLUTION
Abstract
mining yang penting. Akan tetapi, untuk menentukan jumlah cluster akhir merupakan suatu tugas
yang menantang. Oleh karena itu, penelitian ini bermaksud mengusulkan algoritma novel modified
differential evolution (NMDE) dan algoritma k-means (NMDE-k-means) pada analisis cluster
otomatis. Algoritma ini dapat menentukan jumlah cluster akhir dan melakukan pengelompokan
data secara otomatis. Pada prinsipnya Algoritma NMDE akan melakukan pencarian global untuk
menemukan jumlah cluster dan partisi data, sedangkan algoritma k-means akan memperbaiki
kinerja algoritma NMDE dalam menentukan centroid cluster. Empat data set yang sudah dikenal
yaitu Iris, Wine, Glass dan Vowel digunakan untuk memvalidasi algoritma ini. Hasil komputasi
menunjukkan bahwa algoritma ini lebih baik dibandingkan dengan empat algoritma cluster
otomatis lainnya yaitu improved automatic clustering based differential evolution (ACDE),
automatic clustering using differential evolutionandk-means (ACDE-k-means) dan algoritma
cluster otomatis yang berbasis particle swarm optimization (PSO) serta genetic algorithm (GA)
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