<![CDATA[Transaction data regarding product sales every day will continue to increase and are usually only used as archives, not properly utilized the sales transaction data. The very large number of sales transactions makes it impossible for humans to read and analyze manually. Data regarding sales transactions, if dig deeper into the transaction data, will definitely get important information, such as buying patterns made by consumers. With these problems, therefore we need a system to manage product sales transaction data, based on the tendency of products that appear simultaneously in a transaction using an a priori algorithm. Market Basket Analysis is the process of analyzing transaction data to obtain product purchasing patterns with other products that are usually frequently purchased by consumers, as well as to obtain correlations and associations between these product items. The a priori algorithm is used to obtain an association rule for data mining, where the rules for a combination of an item are calculated for their support and confidence values. With the a priori algorithm that is used, it can find product recommendations from the calculation of the frequent value of a product with other products based on consumer purchase transactions. The results of this study are able to analyze the pattern of product purchases made by consumers and can provide convenience in making decisions for future marketing strategies.]]>
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