Abstract
The process of managing the pattern of handling stock of goods and the pattern of arranging goods on store shelves requires an identification process by utilizing data from sales transaction results. Market basket analysis of sales transaction data using Apriori Algorithm stages produces an information in the form of association rules with a minimum support value of 50% and a minimum confidence of 60%. It can be a reference in the arrangement of items on store shelves by referring to a combination of items that are often bought by consumers simultaneously. In addition, the stock inventory pattern can take advantage of the results of determining the high frequency value in the combination pattern 1 - itemset C1 with a minimum support value of 50% which is compared with the initial inventory.
Highlights
In the management of a minimarket, it is necessary to have a system management in the process of handling the patterns of stock handling and patterns of arrangement of goods, with the aim of structuring the goods to make it easier for customers to shop and patterns of handling stock of goods to provide the availability of goods needed by customers
Sales transaction data can be used to help decisions in predicting the layout of goods so that consumers find the items sought and determine the prediction of the amount of stock in the future. These problems can be solved by market basket analysis using the stages of Apriori Algorithm, namely by identifying the value of support and confidence of goods sold at the minimarket
It can be a preference in the pattern of arrangement of goods based on customer habits in buying goods simultaneously and can be a prediction of the stock of goods in the future [1]
Summary
In the management of a minimarket, it is necessary to have a system management in the process of handling the patterns of stock handling and patterns of arrangement of goods, with the aim of structuring the goods to make it easier for customers to shop and patterns of handling stock of goods to provide the availability of goods needed by customers. Sales transaction data can be used to help decisions in predicting the layout of goods so that consumers find the items sought and determine the prediction of the amount of stock in the future. These problems can be solved by market basket analysis using the stages of Apriori Algorithm, namely by identifying the value of support and confidence of goods sold at the minimarket. It can be a preference in the pattern of arrangement of goods based on customer habits in buying goods simultaneously and can be a prediction of the stock of goods in the future [1]
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