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All Journal Jurnal Teknik Informatika dan Teknologi Informasi
Tety Citra Natha
Universitas Palangka Raya
Computer Science & IT

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Penerapan Algoritma Association Rule Mining (Apriori) untuk Analisis Pola Pembelian (Market Basket Analysis) pada Data Transaksi Ritel Jadiaman Parhusip; Tyara Rahmidasari; Erina Ekanova Safitri; Tety Citra Natha; Nur Haniatin Jannah
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6362

Abstract

Market Basket Analysis (MBA) is a data mining technique used to identify product combination patterns that frequently occur together within a single transaction. This study applies the Apriori Algorithm as the primary method for discovering Association Rules from a large-scale e-commerce retail transaction dataset consisting of approximately 500,000 records, focusing on consumer purchasing behavior in the United Kingdom market. The research follows a systematic data mining process that includes data integration, data cleaning to remove anomalies such as negative prices and quantities, and data transformation using One-Hot Encoding to convert transaction records into a suitable binary matrix format. The Apriori Algorithm is then used to generate frequent itemsets, which are evaluated using Support, Confidence, and Lift to determine their strength and significance. The results show several strong Association Rules with Lift values greater than 1.0, indicating positive correlations between specific product pairs. These findings offer useful insights into consumer purchasing tendencies and can support various retail strategies, such as improving product recommendation systems, optimizing store layout, enhancing promotional bundles, and strengthening targeted marketing efforts. Overall, this study demonstrates that Apriori-based MBA is capable of extracting actionable knowledge from large-scale retail datasets and contributes to more effective, data-driven decision-making in the retail sector.
Co-Authors Erina Ekanova Safitri Jadiaman Parhusip, Jadiaman Nur Haniatin Jannah Tyara Rahmidasari