Data Mining Sales of Skin Care Products Using the K-Means Method

Abstract

Data mining is a form of method advancement in computerization that can dig past data into very valuable information. The problem in this study is that the sale of beauty and skincare carried out by Toko Hayati Store is still done manually so that it can cause it to not match the stock in the storage warehouse with changing market demand. Data mining with the K-Means method is one solution to this problem by grouping similar data, in this study grouping into two, namely best-selling and unsold products. The purpose of this study is that the store can provide stock of products in the warehouse according to market demand. Using a sample of 30 data resulted in 18 data as skin care products were not selling well and 12 data as skin care products were not selling well. With the results of a 100% similarity between manual calculations and using the rapid miner application, it can be concluded that the K-Means algorithm can be used as a solution to the problems that exist in the Toko Hayati Store.