Abstract
The variety of stationery marketed, makes business competition increasingly fierce in order to provide the best service to customers. Abundant sales transaction data, triggering piles of data so that it requires data mining processing techniques, namely association rule mining using the FP-Growth algorithm. Algorithm that generates frequent itemset used in the process of determining the rules that can produce an option by taking a product sales transaction data object. The test results show a rule that has the best confidence value and lift ratio of 100%, as well as 80% support with the rules that every purchase of a ballpoint product can be sure to buy a notebook from the dataset used as a sample data in the system trial (50 names). goods and 7 transaction data) with minimum support (5% = 0.05) and minimum confidence (30% = 0.3).
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