<![CDATA[Analyzing piles of sales transaction data turns out to be able to produce information, one of which can take a recommendation and layout decisions of goods such as arranging goods according to association patterns, doing discount or cheap redemption prices on products that are less desirable based on the results of the association pattern, and information on goods that are not in demand. less desirable according to association rules. The association pattern has several solutions, one of which is using the fp growth algorithm. The purpose of using the fp growth algorithm is to find out frequent itemset data sets, in this study the authors apply the fp growth algorithm and association rules to cooperative data for the 1 day period of 2019. The results of this study are to produce applications that can make it easier for cooperatives to take A decision uses 20 sample data to look for association rules and FP growth, which can analyze consumer habits in making purchases, with an average percentage of support values of 9.09% and a confidence value of 100% Keywords: Data Mining, Fp Growth, Association rules, recommendations]]>
Copyrights © 2022
