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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal SOLMA JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Firman Nurdiyansyah, Firman
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Other

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Analysis of Milkshake Beverage Sales using Apriori Algorithm Sujito, Sujito; Idris, Muhammad; Kadir, Shaifany Fatriana; Nurdiyansyah, Firman
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research discusses the application of Data Mining with the Apriori algorithm on milkshake drink sales to support Business Intelligence. The research process includes collecting sales transaction data, forming frequent itemsets, and analyzing association rules using metrics such as support and confidence. The results show that product combinations, such as Chocolate and Strawberry, have high purchase rates with support reaching 75% and confidence up to 75%. These findings provide important insights for business owners in designing more effective marketing strategies, including promotions and stock management optimization. By utilizing the Apriori algorithm, this research successfully identified significant purchase patterns that can drive growth and improve customer satisfaction in the food and beverage industry.
Analysis of Puchase Patterns on Office Stationery Sales Data using Apriori Algorithm Wahyudi, M. Ilham Setyo; Nurdiyansyah, Firman; Kristianti, Dini
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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This study analyzes purchasing patterns in office stationery sales using the Apriori algorithm, a data mining method for generating association rules and frequent itemsets. The research examines transaction data to identify combinations of frequently purchased items, aiming to improve inventory management and marketing strategies. The Apriori algorithm calculates metrics such as support, confidence, and lift to determine strong associations between items. Results indicate key purchasing patterns, such as frequent copurchases of notebooks and pencils, which inform targeted promotions and stock planning. The findings highlight the potential of data-driven decisionmaking to enhance business efficiency and customer satisfaction in the retail sector.
Co-Authors Ahmad Fairuzabadi Akbar, Ismail Aviv Yuniar Rahman Bagas Imadani Putra, Alif Dini Kristianti, Dini Fatriana Kadir, Shaifany Istiadi Ma'rifani Fitri Arisa Muhammad Idris Muslim, Shinta Nilam Sari Putra, Lionardi Ursa Sofyan Rachma Danni, Muhammad Sujito Sujito Syah, Adryan Verdiansyah, Egi Wahyudi, M. Ilham Setyo