Implementation of Association Rule Mining in Determining Goods Layout Patterns Using The Frequent Pattern Growth Method
DOI:
https://doi.org/10.71302/jbidai.v8i2.72Keywords:
implementation, association rule, fp-growth, layoutAbstract
CV. HKA (CV. Harapan Kaltim Abadi) is a distributor that sells several skin care, hair care, perfume, deodorant and baby care products, which will be distributed to customers. CV. HKA does not yet have a layout pattern for goods in the warehouse so that the storage of goods in the warehouse is irregular. Arranging the layout pattern of goods is needed to speed up the helper's performance so that it can shorten time and make it easier to pick up goods. Therefore, we need a system that can help determine item layout patterns by applying data mining. Association rules are used to search and find relationships between items in a dataset. The application of data mining with association rules aims to find information on items that are related to each other in the form of rules. Frequent Pattern-Growth (FP-Growth) is a method that can be used to determine the data set that appears most frequently (frequent item set) in a data set. This method aims to determine the layout decisions often taken by the helper. The results of the application using the FP-Growth method are rules that explain the helper's tendency to take goods simultaneously. These rules will determine the placement of goods in the warehouse. Based on the analysis system that has been created, it produces 10 rules from a combination of 2 items with the highest confidence value of 0.63, namely the Caplang brand item MKP and the Mandom brand item MR so that the two brands can be recommended to be placed close together. Apart from that, the resulting lift ratio value is more than 1, namely 1.21, the higher the lift ratio value, the stronger the resulting association rule.
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