CLUSTERING OF CLOTHING SALES DATA AT TOP STORE USING K-MEANS METHOD

Abstract

In the era of globalization, the development of technological sophistication is growing rapidly which is an aspect that can be utilized to achieve convenience, especially in the flow of information. This technological sophistication by all accounts is increasingly spreading with the use of computers which are currently very popular in various areas of life. For example in the fields of education, entertainment, health, especially in the business sector. Top Store is a store that is engaged in selling clothes, however, of the various types of clothes that are sold, of course, not all of them are selling very well, and some are not selling well. Sales data, purchase of goods and unexpected expenses at Top Shop is not structured, so the data are only serves as an archive for the store and not be utilized for the development strategy of marketing. Therefore, it is necessary to apply Clustering of Clothing Sales Data in Top Stores with the K- Means Method . The K-means method can be applied to Top Stores to determine which clothes are selling very well, selling well and not selling well. The application of the K-Means method in Top Stores, namely by grouping clothing stock data. Then choose 3 clusters randomly as the initial centroid. After the data in each cluster does not change, it can be seen that the final result is that there are 21 best-selling articles, 17 articles that are selling well and 12 articles that are not selling well. Then applying the K-means method to Rapidminer is done by entering product stock data, namely initial stock, sold stock and final stock which will become a database on Ms. Excel, the data is then connected to the Rapidminer Tools , and will be processed and formed K-means.