| Abstract | UMS Store is an official trading business unit owned by Muhammadiyah University of Surakarta that provides various categories of books, journals, office stationery and official marchandise. UMS Store is also a voucher exchange center for students. Of the many voucher redemption transactions and cash purchases, UMS Store has abundant data and will continue to grow over time. The abundant data if left unchecked would be a pile of only stored data. Actually, if the data is excavated will produce valuable information. UMS Store need a customer grouping application that will be used to provide continuous treatment such as giving discounts or vouchers to their best customers. This research is done to create the application, where application can make customer grouping with UMS Store data and can give recommendation through potential group that formed. This application was developed by utilizing K-means algorithm, which is one of clustering method in data mining technique. Groupings made in the application are limited to 3 large groups of data with restrictions using only student data using UMS Store vouchers. Variables used consist of NIM, year force, discount, sub total, total paid, total item and date.The results of this study is an application used to classify customers using K-means method. The results of this study indicate that if the application is used to create three groups, it will form three clusters, ie clusters of potential customers, normal customers and unlikely customer clusters.DOI: https://doi.org/10.26905/jtmi.v4i1.1772 |
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