Study of application of data mining market basket analysis for knowing sales pattern (association of items) at the O! Fish restaurant using apriori algorithm

Abstract

The development of the food and beverage culinary industry is growing very rapidly. Making food and beverage business owners, especially restaurants, have to make the right decision to stay in a very strong competition, restaurant owners must be ready to always innovate and remain to be able to meet consumer needs through products that can attract customers and determine strategies promotions that can boost sales. Stored transaction data has information that can be extracted by data mining techniques, for example knowing the pattern of sales in purchases by consumers. Information about sales patterns can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to items (menus) that are often purchased together. . To be able to find out the purchase patterns by consumers simultaneously, knowing what products are often purchased simultaneously can be used data mining techniques using a priori algorithms. A priori algorithm is used to generate association rules. Information about the association's rules in purchasing items (menus) by consumers can be used by O! Fish restaurants to create more potential promotional strategies to boost sales by referring to a combination of items that are often purchased simultaneously. Later the results of this study are in the form of a website-based application to analyze purchasing patterns (item association rules) by consumers where the purchase pattern can be used as recommendations in determining the promotion development strategy for O! Fish restaurants.