Implementasi Metode K-Means Clustering Untuk Pengelompokan Data Penjualan Pada Minimarket Remaja Kampus Bengkulu

Roki Aprinsa, Siswanto Siswanto, Ila Yati Beti

Abstract


Campus Youth Minimarket is one type of business in the field of selling daily necessities. For decision making in determining the amount of product inventory that can be adjusted to market demand, the Campus Youth Minimarket has not used the system and is still calculated manually. Therefore, this research was conducted with the aim of implementing the K-means Clustering method in grouping sales data at the Bengkulu Campus Youth minimarket. So that it can easily determine and classify high, medium and low product sales. The implementation of the system uses the PHP programming language and MySQL database and the method used in this research is the waterfall method. After the K-means process was carried out at the Campus Youth Minimarket with 15 data data tests, 3 clusters of goods were obtained, namely cluster 1 as a high sales cluster with 7 items, cluster 2 with moderate sales of 4 items and 4 items in a low sales cluster. Based on the results of processing 278 data on sales of goods in December 2021 at the Campus Youth Minimarket using the K-Means Clustering Method, the results of the grouping of product sales levels at the Bengkulu Campus Youth Minimarket were 3 clusters. Namely cluster 1 group with a high level of product sales with a total of 54 product data, cluster 2 with a moderate level of product sales with 165 types of products and cluster 3 with a low level of product sales with 51 total products. Based on the data cluster, it can be used as a reference by the Campus Youth Minimarket for the following month's product inventory. Which product clusters that have a high level of sales have a high or stable number of orders as before. Then product clusters with low sales levels, then the amount of product inventory for the next is reduced so that there is no accumulation of products in the warehouse and experiencing expiration.

Keywords


Data Mining, K-Means Clustering, Remaja Kampus Minimarket

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References


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DOI: https://doi.org/10.34007/incoding.v2i2.302

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