Penerapan Algoritma K-Means Clustering untuk Menganalisis Penjualan pada Toko Ayu Collection Barbasis Web - Wahyu Tities Pambudi; Arita Witanti (Jurnal Informatika Universitas Pamulang, 2021)
The main activity in business is to determine the amount of stock that must be maintained to analyze the profit of each item sold. Therefore, groups such as high and low sales categories are needed to consider the stock of goods in the sales process. Ayu Collection store is a store that sells various types of clothing and accessories that have not implemented the grouping of goods in its sales information system to provide the maximum and the minimum number of stock items to be sold at the store. The process of grouping goods is still done manually, which is based on observations from shop owners. Therefore, to maintain the stock of goods so that no items are empty, this study aims to support the process of determining the stock of goods by building a model that can group items into high and low categories in sales using k-means clustering. The group with the highest centroid will be the group with the highest selling rate, while the lowest centroid will be the group with the least demand in sales. The data used in this study was taken from sales data in 2017 and 2018. The clustering scenario uses the variable name of goods, data of incoming goods, data of goods out, and stock of goods. The results of this study are showing the value of system performance in grouping goods by 83.33%.