<![CDATA[Customer satisfaction is a key pillar of success in the competitive hospitality industry, directly impacting loyalty and profitability. Recognizing this, Platinum hotels need the ability to predict guest satisfaction in order to refine their service strategies. This study focuses on the development of predictive models of customer satisfaction at Platinum hotel using the K-Means Clustering method. This method was chosen because of its effectiveness in grouping complex data into homogeneous segments based on common characteristics. Customer Data will be grouped by attributes of their stay to identify different segments of customers with unique levels of satisfaction and preferences. It is hoped that this model can provide deep insights into customer profiles, reveal hidden patterns, and predict future guest expectations. The results of this study will contribute to improving the quality of Service and strategic decision-making at Platinum hotels and can be a reference for the hospitality industry in implementing a data-driven approach.]]>
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