Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids
DOI:
https://doi.org/10.30865/mib.v6i1.3294Keywords:
K-Means, K-Medoids, Clustering, Algoritm, Data MiningAbstract
As a business company, PT. XYZ Indonesia is committed to always make improvements to all aspects such as, in terms of determining superior products. To be able to do this, it requires a sufficient source of information to be able to analyze what products are superior or in high demand and what products are less desirable. To find out what products enter the superior product cluster, then researchers do product grouping using the clustering method. In the clustering method there are two types of cluster analysis that have interrelated algorithms, namely k-means and k-medoids. The result of research already conducted that from the value of Davies Bouldin to the k-means algorithm is -0.430 and from the value of Davies Bouldin k-medoids is -1,392 which means that the Davies Bouldin value for the k-medoids method has the smallest Davies Bouldin value so the grouping results using the k-means method are more appropriately used on the issue of superior product selection.References
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