Decision Support System Metode K-Means Clustering Dalam Memprediksi Tingkat Penjualan Pada Store Ryan Mart

Abstract

Ryan Mart store is one of the stores located in the god's fence that sells grocery, drinks, food, perfume, and electric tools. Where the Ryan Mart store is currently going to make an application in predicting the level of sales where management of the inventory of goods in the Riyan Mart store, all this time is still felt manually, that is, if the inventory runs out, then orders will be made to the supplier of goods in the city of Bengkulu. . In sales, there needs to be an effectiveness in predicting the level of sales of goods at the Riyan Mart store, an application is needed that can help provide the level of daily sales at the Bengkulu City store Riyan Mart, from applications designed using the K-Means Clustering method it will be known what items should be improved in future sales of goods. The K-Means Clustering method can help predict the level of sales at the Ryan Mart store. days, and then the data will be grouped based on 3 (three) clusters, namely Cluster I (Most Interested Items), Cluster II (Items That Are Least Interested), Cluster III (Items Not Interested). In the implementation of the K-Means Clustering algorithm, it is done by grouping data based on sales of goods, where before grouping the data, it will be added automatically to get the results of the number of sales of each item in the next period, which is 1 year. To assist in the implementation of the K-Means Clustering algorithm, a Visual Basic .Net application was created with the SQL Server 2010 data base.