Implementasi Algoritma K-Means untuk Pengelompokan Data Penjualan (Studi Kasus : Toko Sembako Sudiawati)

Abstract

Sudiawati Grocery Store is a business engaged in household needs. Sudiawati Grocery Store has a distribution center. This distribution center stores many different products to be sold, looking at the store distribution center still involves bookkeeping for various transaction information, and still reviewing the products to be purchased to fill the stock at the distribution center, there is still no estimate for the products that are generally sought after by customers, to generate product developments that are currently popular and minimize infrequently purchased products that cause losses. This problem is caused because the system is still manual in transacting in every sales data in Sudiawati Grocery Store to be able to overcome the problems that occur, then Sudiawati Grocery Store requires a system that can manage and store data, so that errors do not occur frequently in managing sales data. This study aims to design a sales data management system that can facilitate data collection on best-selling products at Sudiawati Grocery Store by using the CRISP-DM system development method and using the k-means clustering method to classify best-selling sales data.