Data Mining Approach to Improve Minimarket Sales using Association Rule Method

Harlinda Harlinda; Ramdan Satra

doi:10.31294/inf.v12i1.20835

Data Mining Approach to Improve Minimarket Sales using Association Rule Method

Harlinda Harlinda, Ramdan Satra

Abstract

This research aims to provide recommendations for the placement of goods sold by the UMI Faculty of Computer Science mini supermarket. A data mining approach is used to determine the position of sales items between related items. This is done to make it easier for customers to search for items to buy based on the type of item. Another problem is determining the best-selling items and also determining the types of items that will receive promotions. The data mining approach uses association rules with a priori algorithms. Association rule mining is a data analysis technique used to find patterns and relationships in big data. This technique is widely used in business to help optimize marketing and sales strategies. The results of the rule association using an a priori algorithm show that if consumers buy 200 milli of Ultra Milk Slim Chocolate, they also buy 600 milli of LE MINERAL with a support value of 10% and confidence of 60%. This shows that these two items are related when consumers purchase.

Keywords

Sales, Association rule mining, Apriori algorithm

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DOI: https://doi.org/10.31294/inf.v12i1.20835