Optimization of Data Mining Models in Identifying Consumer Shopping Patterns in the Retail Sector Using the FP-Growth Method
DOI:
https://doi.org/10.58982/vj1pe417Keywords:
Data Mining; Fp-Growth; Assosation RuleAbstract
Intense competition in the trading industry makes business actors to carry out more effective sales strategies to face market competition. One strategy that can be used to face market competition is to utilize data mining information technology with the association rules method. Data mining is the process of collecting, using historical data, and modeling large data sets to obtain new patterns or knowledge that can be utilized later. UD. Kori conducts dozens more sales transactions every day. Sales transactions that occur every day produce increasing data in the database and the availability of goods often does not meet consumer needs, making consumers switch to other stores. This study aims to apply the Fp-Growth Algorithm to determine consumer buying patterns based on sales transaction data. Sales transaction data used as many as 34,085 transactions. Through the mining process with the Fp-Growth algorithm, it will be obtained what products are often purchased simultaneously by consumers and the tool used in this study is Google Colaboratory using the python programming language. The results of the study found that products that are often purchased simultaneously are Mie Sedap Kuah Rasa Soto and Indomie Goreng have support of 0.29% and confidence of 97% with the highest lift ratio reaching 159.73.
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