Main Article Content
In some companies that have sales transaction data and this data will increase from day to day so that it will accumulate and become garbage if it is not managed and utilized properly. Sales transaction data is one thing that can be used to increase product sales. Not only to increase product sales but also to provide product recommendations for each sale. As in the product stock setting section, it can provide recommendations for the number of products so that problems such as over stock will not occur which will cause the amount in a product to expire. In this study, an association rule data mining will be implemented for cosmetic product recommendations using the Apriori algorithm. Testing the results of using data mining and the Apriori algorithm is carried out to find out that the results of the study can find association rules from existing datasets to recommend cosmetic products. The association rule method is used in the search for product attachment patterns for sales strategies in policy decision making. So that it can be seen that the cosmetics that are often purchased by consumers, based on the rules generated from the data contained in the database. Tests were carried out using the Rapidminer 9.5 application. The results obtained from this test are that there are 16 rules (rules) that will be used for decision making in cosmetic product recommendations.
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