The Use of Association Rule Mining in Determining Price, Promotion, Stock, and Product Arrangement in Store

Abstract

as consideration in determining the more effective sales and marketing strategy. Product arrangement at Anterah store at the moment is based on product group which has the same type and no analysis about the correlation between the products bought by consumers to arrange those products in the store. Product placement that has associative correlation to declare that the product at the same time is bought by consumers become important to be done. With the product at the right product arrangement, which is by arranging the layout of the product which are bought at the same time by consumers nearby, they can choose and take the products without looking for them in the different location (it takes more time for the consumers). Abstract: In the first quarter of 2017, the retail market in Indonesia increased 2.5% while in 2018 the growth was only around 1% -1.5%. The cause of this slow growth was the shift of the patterns of society consumption and it was still continuing in the early 2018. Besides that, it was also because of the decrease in productivity in the lower-class society. As a retail store, Anterah shop was also dealing with the same problem so that the analysis of the most sold product and how to figure out the correlation between the product bought by consumers are needed to anticipate a decline in sales. Associative correlation between these products will be used as the basic of the product arrangement, so that the frequency of products which are bought most frequently by consumers at the same time can be arranged nearby to make the consumers get the product quickly. Market basket analysis to figure out the correlation between the product which are sold at the same time, Association Rule Mining is issued, it will produce the product which will be bought at the same time as the consideration of product arrangement in the store of Anterah retail store. Meanwhile, the most sold product which will be mined by using Frequent Pattern Growth method so that the rating list of the most sold products will be obtained. This analysis is used as the background in considering product promotion. Test results on the sales sample data is shown as follows: minimum support = 0.0025, minimum confidence = 0.610, LaPlace = 0.9985, Gain = -0.006, p-s = 0.003, Lift = 103.82, Convicting 2.5285 with processing time 41.456 seconds. two products that are purchased at the same time. They are products like granulated sugar, Indomie (instant noodle), toothpaste, milk,