Classterization of best-selling sales data for your cosmetic products using the k-means algorithm

Abstract

There are many types of businesses in Indonesia that attract people's attention and increase competition by creating creativity in various business ideas. The Indonesian cosmetics and skin care company is a Y.O.U brand dedicated to the beauty of Indonesian women, produced according to people's needs and current trends. The task of data mining is to collect or group large amounts of data based on various predetermined criteria. Cluster data mining is ideal for algorithms that can process data more efficiently, namely the K-Means algorithm. The K-Means Clustering algorithm is a method used to combine several data streams to analyze data and determine the number of Clusters that have different characteristics from other data. The expected result is to have a group of data that will influence the sales strategy in the next period so that you can find out which items are best sold and which items are less popular with customers. This decision will minimize the losses previously received by PT Lintas Mandiri primarily on YOU cosmetics. With this data, it can be grouped into 3 clusters based on the level of sales of a type of product. Cluster 0 consists of 8 product items that have the best selling level, while in Cluster 1, which includes 12 product items in the best-selling category, and Cluster 2, which consists of 5 items. Products that are not selling well in this Cluster show a steady or even decreasing trend from month to month with the Centroid value indicating the unpopularity of these products in the market.

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