%L eprintsunpak8103
%D 2024
%I Universitas Pakuan
%X Sistem Rekomendasi Menu Makanan dan Minuman dengan 
Metode K- MEANS dan FP-GROWTH pada Cafe Milarian
Asep Denih1)
, Irma Anggraeni 2)
, Yudiansyah3)
1,2,3 Program Studi Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, 
Universitas Pakuan
1,2,3 Jl. Pakuan No.1, Ciheuleut, Bogor, 16143
Email: 1asep.denih@unpak.ac.id, 
2
irma.anggraeni@unpak.ac.id, 
3
yudiansyah04@gmail.com
Research aims to enhance operational efficiency and customer experience in a cafe 
through the implementation of data mining-based technology. The two main methods used 
in this study are K-Means Clustering and the FP-Growth Algorithm. K-Means is 
employed to group customers based on their menu preferences and purchasing behavior, 
while FP-Growth is used to analyze frequently occurring purchase patterns. The 
implementation of K-Means enables the cafe to devise more targeted marketing 
strategies, adjusting menus and promotions to meet customer needs. Additionally, FP�Growth aids in identifying relationships between frequently co-purchased menu items, 
allowing the cafe to create more enticing bundled offers and boost cross-menu sales. The 
implementation of this technology is expected to have a positive impact on customer 
satisfaction, increase sales, and optimize inventory management in the cafe. By 
combining these two methods, cafes can improve operational efficiency, provide a better 
customer experience, and compete effectively in the increasingly competitive cafe 
industry.
Keyword : Caffe Technology Efficiency, K-Means, FP-Growth, Product 
Recommendation
%T Sistem Rekomendasi Menu Makanan dan Minuman dengan 
Metode K- MEANS dan FP-GROWTH
%A Yudiansyah Yudiansyah
%A Irma Anggraeni
%A Asep Denih