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All Journal Journal of Data Mining and Information Systems
Elya Septi Mulyani
Universitas Bengkulu
Computer Science & IT

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Analisis Pola Asosiasi Transaksi dengan Algoritma Frequent Pattern Growth pada Dataset Takeaway Food Orders Elya Septi Mulyani; Natasya Apriesti Putri; Elvi Rariska; Etis Sunandi
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7390

Abstract

The rapid expansion of the takeaway food industry through digital platforms has generated a vast volume of transaction data. A significant challenge lies in efficiently extracting valuable insights from large databases without overtaxing computational memory. This research proposed the implementation of the Frequent Pattern Growth (FP-Growth) algorithm to map association patterns between food items without the need for iterative candidate itemset generation. The experiment was conducted on the Takeaway Food Orders dataset from an Indian restaurant in London, comprising 13,397 transactions and 74,818 data rows. The methodology involved data pre-processing, FP-Tree construction, and the determination of strong association rules. Results demonstrated that the algorithm effectively identified customer ordering trends. By applying a 3% support threshold and 75% confidence, seven significant association rules were identified, with Plain Papadum serving as the consequent in all rules. These rules indicate that Plain Papadum has a high tendency to be ordered together with Mint Sauce, Onion Chutney, Mango Chutney, Pilau Rice, and Red Sauce. This study concluded that FP-Growth-based data mining provides accurate strategic insights for business owners to optimize promotional bundling and enhance customer loyalty.
Co-Authors Elvi Rariska Etis Sunandi Natasya Apriesti Putri