<![CDATA[The sales pattern becomes a benchmark for increasing product sales in a company. Sales patterns are one way that can be used to determine sales strategies by looking at how often an item is purchased simultaneously. The large number of daily transactions makes it difficult for companies to determine sales strategies. Data mining analyses extensive data to find relationships between data and can produce valuable information. Patterns of sales or consumer transactions look for relationships between one product and another in one transaction using the Association Rule method. The algorithm used is the Modified Apriori Algorithm. The data used is transaction data on capacitor products. The data used is 15513 transactions with the variables LotNo and Material Code. Processed with the Python programming language and Flask as the user interface, the minimum support used is 0.01, and the minimum confidence is 0.5, resulting in rules with the lowest reliability of 50% and the highest reliability of 100%. Based on the results of a comparison of the performance time of the Modified Apriori Algorithm and the Classic Apriori Algorithm in processing 15513 transaction data with the given conditions, namely minimum support = 0.02 and also minimum confidence = 0.5 with the time obtained by the Modified Apriori Algorithm for 5 minutes 5 seconds and the Classic Apriori Algorithm for 6 minutes 6 seconds]]>
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