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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Nizar, Mohammad Nabil
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Data Penjualan Sepatu pada Toko MNNZR.ID Menggunakan Algoritma Apriori dan FP-Growth Nizar, Mohammad Nabil; Sarwido, Sarwido; Sucipto, Adi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.7014

Abstract

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.
Co-Authors Adi Sucipto, Adi Sarwido Sarwido, Sarwido