<![CDATA[The K-means Clustering algorithm technique is being used in this study to gauge how satisfied students in the Universitas Perintis Indonesia Digital Business programme are with the cafeteria's offerings. The study focuses on customer service characteristics such meal quality, cost, speed of service, cleanliness, and comfort in the cafeteria setting. The goal of the research is to provide deeper insights into student expectations and preferences for cafeteria services by utilising K-means to uncover distinct satisfaction patterns among student groups. When used to measure student satisfaction with cafeteria services, the K-means Clustering method is successful at identifying groups of students who have similar patterns of satisfaction. Some student groups score food quality and cleanliness favourably, according to the clustering data, while other groups may be more critical. In light of the preferences of each student group, cafeteria management can use this data to develop more specialised plans for improving services. The study also shows that using the K-means Clustering method to evaluate customer satisfaction offers a potentially advantageous strategy for enhancing service quality across a variety of service sectors.]]>
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