The Discovery of Association Rules and Data Analysis to Optimize Japanese Food Sales

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Narin Jiwitan


This research aims to study the association between each food menu sold in a Japanese restaurant by using The FP – Growth Algorithm. The research methodology was based on the CRISP-DM. Data were from 4,254 receipts which were 16,409 food items sold. As a result of data cleaning, there were 13,377 items left to analyze. The result found that the food purchase had eight association rules when using 0.05 as the minimum support and 0.20 as the minimum confidence. With the highest confidence value, it can be concluded that if customers bought Tuna, it was likely that they would purchase Salmon too, with a confidence of 52.94%, a lift of 5.01. There was a dependent relationship between Tuna and Salmon. Additionally, Crab Rangoon was the most sold item in the restaurant. Set Punpla was the most sold set menu. Customers usually dined in during 6-7 PM and paid by cash. The result of this study could be utilized in restaurant promotion and menu suggestions to customers, which would help the restaurant’s competitive advantage, increase sales volume, make marketing strategies, and find new business opportunities.


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N. Jiwitan, “The Discovery of Association Rules and Data Analysis to Optimize Japanese Food Sales”, JIST, vol. 12, no. 1, pp. 1–12, Jun. 2022.
Research Article: Information Systems (Detail in Scope of Journal)


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