PRODUCT CLUSTERING IN THE MSME BUSINESS OF GROCERY STORE

Abstract

The business world is an exciting world to follow due to its dynamic and competitiveness. Sri Wahyuni Grocery is one of the MSMEs involved in buying and selling daily-basis needs. The business can manage an average of 115 transactions in a day, including various transactions for necessities. Most products purchased at this grocery shop are daily basic needs, such as sugar, tea, instant food, snacks, and fuel oil. It is known that from observations, some products are less desirable by buyers so they are not selling well and some products are selling well, so it is necessary to do a product grouping process to find out how to group these products. An analysis of existing data is needed to obtain information with the K-Means Clustering algorithm. This research aims to determine the pattern of transaction data owned by Traditional Grocery Store MSMEs and form a Clustering pattern of products offered by Traditional Grocery Store MSMEs. Through the data exploration process, this research will carry out a pattern search from transaction data and clustering patterns owned by Traditional Grocery Store MSMEs. Based on the findings from the research conducted, business actors will be able to develop various strategies to improve services to sales by relying on the use of various data mining algorithms. The research was conducted on Traditional Grocery Store MSMEs with transaction data for two months, and the research carried out data exploration to determine clustering patterns using the CRISP-DM method.