The CLUSTER ANALYSIS OF SALES TRANSACTION DATA USING K-MEANS CLUSTERING AT TOKO USAHA MANDIRI - Fithri, Fauzia Allamatul; Wardhana, Sukma (Jurnal Pilar Nusa Mandiri, 2021)
Data mining is a process to obtain useful information from a database warehouse in the form of knowledge. Data transaction history of sales can be information for a business decision. Toko Usaha Mandiri has a problem with the stock of goods, and there are passive goods that settle in the warehouse for a relatively long period. Previous research conducted data analysis to form data clustering into useful information. This study aims to analyze sales data by applying the K-Means Clustering algorithm to form sales clusters. The results of data clustering form cluster1, cluster2 and cluster3 with percentage values of 62% (11 data), 8% (56 data) and 30% (25 data), respectively. Cluster validation of K-Means Clustering algorithm with Davies Bouldin Index produces a value of 0.2. The information of sales clustering can be an alternative solution, input for stock management and marketing strategies.