PENERAPAN ALGORITMA FREQUENT PATTERN GROWTH PADA POLA PEMBELIAN KONSUMEN (STUDI KASUS G.I.B STORE KOTA CIMAHI ) - Resa Hardiyanti; Tati Ernawati (Jurnal Informatika dan Teknik Elektro Terapan, 2025)
Transaction pattern analysis constitutes one of the key factors in business decision-making, particularly in retail business decisions. This study aims to identify consumer purchasing patterns at G.I.B store in Cimahi City by implementing the Frequent Pattern Growth (FP-Growth) algorithm as a data mining method to discover associative patterns among products that are frequently purchased together. This research utilized data from 2023 encompassing both online and offline sales transactions, and the research process included data collection, data cleansing, data transformation, and the application of the FP-Growth algorithm using Google Colaboratory, as well as analysis of the resulting association patterns. The findings demonstrate that strong relationships exist between certain specific products, such as between Junior Premium 8 and Kids Premium M, with a confidence level of 77.91%. These patterns can assist in determining and formulating business strategies for promotions, product bundling, and more efficient inventory management. The implementation of the FP-Growth algorithm has proven effective in helping business owners understand customer shopping behaviors and support more targeted decision-making.