Application of Data Mining with Apriori Algorithm and FP Growth on Cafe Bread Sales to Support Business Intelligence

Abstract

This study aims to apply the Apriori and FP-Growth algorithms in analyzing
sales transaction patterns in a bakery Cafe, with a focus on developing a
business intelligence strategy. The data used includes 20,507 transactions
from January 11, 2016 to December 3, 2017. The results of the analysis show
that items (coffee and bread) are the most frequently purchased, with the
highest support values of 26.67% and 32.72%, respectively. In addition,
several significant association rules were found, such as a positive relationship
between (hot chocolate and coffee). This study provides insights that can be
used to design more effective marketing strategies, including bundling
promotions and more efficient stock management, so as to increase sales and
customer satisfaction.