Email this article KLASTERING DATA MENGGUNAKAN ALGORITMA DYNAMIC K-MEANS
Abstract
to have problems determining the initial partition number of
clusters (k) determining the initial value that is different may
produce different cluster groups. To solve the problem of the
sensitivity of the initial partition number of clusters in K-means
algorithm, the proposed algorithm dynamic cluster. The result
showed that the Dynamic K-means algorithm, can produce quality
cluster that is more optimal than the K-means.
Intisari— Salah satu kekurangan algoritma K-means yaitu
mempunyai masalah sensitif terhadap penentuan partisi awal
jumlah cluster(k) penentuan nilai awal yang berbeda mungkin
dapat menghasilkan kelompok cluster yang berbeda pula. Untuk
menyelesaikan masalah sensitifitas partisi awal jumlah cluster
pada algoritma K-means, maka diusulkan algoritma cluster
dinamik. Hasil percobaan menunjukan bahwa algoritma
Dynamic K-means, dapat menghasilkan kualitas cluster yang
lebih optimal dibandingkan dengan K-means.
Kata kunci : Segmentasi Pelanggan, K-Means, quality cluster
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DOI: https://doi.org/10.31294/jtk.v1i2.259
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ISSN: 2442-2436 (print), and 2550-0120
