Implementation of the K-Means Method for Beverage Clustering Based on Calorie and Protein
DOI:
https://doi.org/10.31102/zeta.2025.10.1.19-29Keywords:
Klaster, Klasterisasi, Clustreing, K-MeansAbstract
Recently, the number of coffee shops in big cities in Indonesia has increased. This makes it easier for coffee lovers to enjoy it. With the increasing public awareness of the importance of healthy drinking patterns in preventing diabetes and other diseases, consuming low-calorie drinks has become a prominent trend. This study aims to group the coffee drink menu at Starbucks based on the calorie and protein content of Starbucks drinks. It is grouped into 2 clusters, namely, high and low clusters. In this study, the clustering process of Starbucks drink menu data was carried out by applying the K-Means algorithm. The clustering results can identify members of Cluster 1 and members of Cluster 2. From the tests that have been carried out, it can group the drink menu into 2 clusters based on the amount of protein and calories from Starbucks drinks and help the public choose which drinks are better to consume.
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