Analisis Data Penjualan Sepatu pada Toko MNNZR.ID Menggunakan Algoritma Apriori dan FP-Growth
DOI:
https://doi.org/10.35870/jtik.v10i4.7014Keywords:
Data Mining, Association Rule, Apriori Algorithm, FP-Growth Algorithm, Market Basket AnalysisAbstract
This study aims to analyze consumer purchasing patterns and compare the performance of Apriori and FP-Growth algorithms using sales transaction data from MNNZR.ID shoe store. A quantitative comparative approach was applied to 520 transaction records collected between June 2023 and January 2025. The data were preprocessed and transformed into a market basket format using one-hot encoding, followed by association rule mining with variations in minimum support and confidence. The results indicate that both algorithms generate identical association rules with similar values of support, confidence, and lift. The strongest rule found is (NB, Adidas, Puma) to Nike, with a confidence of 52.63% and a lift value greater than 1, indicating a positive correlation. However, FP-Growth demonstrates better computational efficiency compared to Apriori. These findings show that association rule mining can effectively support data-driven marketing strategies such as product bundling and cross-selling in retail businesses.
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