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

Muhammad Auzhar Rafli Ramadhani (Universitas Widya Gama Malang)
Shaifany Fatriana Kadir (Universitas Widya Gama Malang)
Niken Paramita (Universitas Widya Gama Malang)
Wiwin Purnomowati (Universitas Widya Gama Malang)
Tshering Peldon (Gaupel Lower Secondary School- Bhutan)
Farrel Muhammad Raihan Akhdan (Universitas Islam Indonesia)

JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June

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.