The Discovery of Association Rules and Data Analysis to Optimize Japanese Food Sales
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Abstract
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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References
JETRO Bangkok, “Survey of Japanese restaurants in Thailand 2020,” Japan External Trade Organization (JETRO), pp. 1 – 9, 2020.
S. Suwanaphokin, “Business Plan for Japanese Restaurant,” Independent research is part of curricular education. M.S. thesis, Business Administration Small and Medium Enterprises Program, Bangkok University, Bangkok, Thailand, 2018.
L. Tomar, W. Guicheney, H. Kyarisiima, and T. Zimani. Big Data in the Public Sector, 1th ed., Washington, D.C., USA. Inter-American Development Bank. 2016.
Report on the overall situation of food products in the ASEAN region. Food products in the ASEAN region. Overseas Trade Promotion Office, Myanmar, 2021.
W. L. Winston, Microsoft Excel 2019: data analysis and business modeling. New York, New York: Published With The Authorization of Microsoft Corporation by Pearson Education, Inc, 2019.
B. Syverson and J. Murach, Murach’s SQL server 2019 for developers : training and reference. Fresno, Ca: Mike Murach & Associates, Incorporated, 2020.
C. Helberg, Data mining with confidence. Chicago: SPSS Inc, 2002.
GarcíaS., Julián Luengo, and F. Herrera, Data preprocessing in data mining. Cham Heidelberg New York Dordrecht London Springer, 2015.
N. Kaoungku, “A DISCRETIZATION METHOD FOR ASSOCIATION RULE MINING,” M.S. thesis, Computer Engineering., Suranaree University of Technology, Thailand, 2012.
B. Mahatthanachai, K. Malaivongs, S. Somhom and N. Tantranont, “Association Rule of Subjects Affecting Student Dropout Using Apriori Algorithm,” in Proceeding of 5th Kamphaeng Phet Rajabhat University National Conference, Thailand, pp. 459, 2016.
P.N. Tan, M. Steinbach and V. Kumar. Introduction to data mining, 1st, Pearson Addison-Wesley, 2006.
K. Kongupon, Th. Rakthammanon, K. Waiyamai, “Techniques for collecting frequently occurring set items. by considering the minimum confidence value to support the increase of data,” Information Technology Journal, Vol. 3 No. 2, pp. 7 - 10. 2007.
S. Kongmaneepun, “Finding association rule from the database of purchasing MYHEALTH supplement to customer using the FP-GROWTH algorithm and customer segmentation according to purchasing behavior of MYHEALTH products with RFM techniques of rapidminer: A case study of a pharmacy chain,” Journal of Information Systems in Business (JISB), Vol. 5, No. 4, pp. 21 – 39. 2021.
N. Khamwichai, “Association Rules”, in Practical Data Mining with RapidMiner Studio 7, pp.35 – 40. 2016.
S. Zhang, X. Wu, C. Zhang, and J. Lu, “Computing the minimum-support for mining frequent patterns,” Knowl. Inf. Syst., vol. 15, no. 2, pp. 233–257, 2008.
prim, “5 Ways to Create a Simple Step-By-Step Sales Strategy,” DEMETER ICT, 21-Jan-2021. [Online]. Available: https://www.dmit.co.th/th/zendesk-updates-th/5-steps-to-sales-strategy/. [Accessed: 29-Jan-2022].
“Bluefin Tuna vs Salmon History Comparison 6 Differences 2 Popular Raw Fish,” NobleMono. [Online]. Available: https://www.noblemono.com/blogs/news/bluefin-tuna-vs-salmon-6-2. [Accessed: 30-Jan-2022].
T. Apiwanworarat. “Japanese Expressions and practice from Japanese Food Culture,” PANYAPIWAT JOURNAL, vol. 5, no. 2, pp. 265–273, Jul. 2014.
Ch. Tokran. Consumption Behaviors of Japanese Consumers in Mueang district Chiang Rai Province. M.S. thesis, Business Administration Department, Sripatum University Chonburi Campus, Thailand, 2008.