<![CDATA[This research is based on observations, currently in conducting sales transactions on average already using a computerized system and recording existing transaction data, but the data only functions as a store archive so that it cannot be used as data to predict the sales results of goods sold. which will be more attractive to consumers. The purpose of this study is to apply data mining to predict the sales results of goods that are more attractive to consumers and to look for relationships between itemsets. From the application of data mining, the data is processed using a priori algorithm to be able to predict the results of the sale of goods. By doing a frequent itemset search using the association rule technique. By determining the candidates that may appear and paying attention to the minimum support and minimum confidence In the application of data mining to predict the sale of goods (researchers managed to find 14 association rules with a minimum support rule of 30% and a minimum confidence of 65%.]]>
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