<![CDATA[Companies have realized how important it is to have a database to face business competition, namely a database system that is reliable and integrated with all parts of the company. One of the data that plays an important role in supporting product analysis, namely customer purchasing patterns. Customer purchasing patterns are important to be able to know the relationship between one product and another. If this pattern has been formed and known, it will greatly help business managers to maintain stocks of goods that are selling well so that they remain available and not empty which can result in the company losing sales opportunities. Transaction data becomes an object to be processed using data mining applications. The data mining method used is association rule mining to find patterns of interrelationships between items, while the algorithm used is the Frequent Pattern Growth (FP-Growth) algorithm. This algorithm applies a tree data structure to determine purchase patterns. The pattern is determined by two parameters, namely support and confidence. The result is a support value obtained of 25% and a confidence value of 100% if the consumer has Pulpy orange 300 ml, the consumer will buy Hydro coco ori. In addition, the support value is 17% and the confidence value is 100% if consumers buy other drinks. The application of the FP-Growth algorithm can help determine what products must be available and assist in managing stock items]]>
