<![CDATA[Stationeries are one of the basic needs in a workspace such as the office and most predominantly in education such as schools. During the beginning of the school calendar, the stationery stores are usually overcrowded by buyers. However, in these times of pandemics people tend to save money by restricting themselves from buying things. As a result, sales tend to drop as fewer people are willing to spend money on goods. One of the ways to increase sales is to observe the buyer's transactions. All of the transaction data are usually kept as an archieve in the stores. On the other hand, the transaction data of the buyers have informations which can be extracted using data mining techniques, such as information about the association rule in the consumer purchases. By understanding the habitude of the consumers, stores are able to consider on the arrangement of their goods. The FP-Growth algorithm which is being used in the shopping cart system will be able to help in developing the marketing strategy as it would observe the associations between items. The FP-Growth algorithm has a sequence of data collection, frequency counter, transaction data rearrangement, tree formation, and frequent item search. From testing the minimum support of 5%, 8 association rules are produced on which 3 of them has a confidence rate above 5%. Subsequently, there are 34 association rules with lift values above 1. The higher of the minimum support and minimum confidence values, the fewer combinations of association rules will be generated.]]>
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