Determining Product Category Sales Patterns to Maintain Inventory Stability Using the fp-Growth Algorithm
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Abstract view : 421 timesAbstract
Managing a very large collection of databases requires a method or technique that can convert piles of data into useful information, one of the data that can be processed is sales data. Anoa Mart mini market is one of the mini markets that participates in meeting consumer needs, so a good way of managing goods is needed to meet consumer needs. One of the data that can be processed is sales transactions at Anoa Mart. Mini Market will provide new information to increase sales. This research aims to determine consumer purchasing habits in order to maintain inventory stability. In this research, the data processing used is Anoa Mart Mini Market sales transaction data. Transaction data is processed using the Fp-Growth consortium Data Mining technique with a confidence value of 70% and a minimum support level of 30%. Processing transaction data produces new knowledge in the form of rules for each item. Each item purchased simultaneously constitutes an association rule that is derived from the value of trust and later becomes knowledge for the small market owner. The model resulting from these rules can be used as a reference to maintain inventory stability and increase sales. This method can be used by small markets to convey information more quickly and accurately, so that sales levels increase and are well controlled.
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