Implementasi Algoritma K-Means Dalam Mengkategorikan Produk Terlaris Dan Kurang Laris Pada Toko Alfamart Cikarang - Ismasari Nawangsih; Reza Puspita; Suherman Suherman (DPPM Universitas Pelita Bangsa, 2021)
Availability of goods and completeness of goods in a shop are very importand elements. This is necessary to avoid the accumulation of the same and less desirable goods. This study aims to see the buyer’s interest in a product so that we can ensure the supply and information of the salable or unsold products. The method used in grouping these products use datamining with the K-Means Clustering method so that the best-selling products can be identified. Product data are grouped based on the similarity of the data so that data same value will be in one cluster. Cluster 1 is a product with a slow moving product and with a central point (18.41 10.43) while Cluster 2 is a product with fast stoct movement or fast moving. Product with a center point (44.69 116.00). With the existence of a product stock cluster with each level of stock movement owned, it is posisible to make a reference in predicting produckt supply according to their needs. The test carried out in this research is black box testing. Keywords: Data Mining, K-Means, Cluster, Product