The growing automotive industry demands workshops to improve the quality of service for customer satisfaction. However, manual measurement of satisfaction is often inefficient and subjective. This study proposes the application of machine learning algorithms K-Means Clustering to analyze customer satisfaction data in Rizal workshop. This method is used to Group customers into several clusters based on similar satisfaction characteristics. The results of this grouping are expected to provide more objective and in-depth insights to identify patterns of satisfaction, thus enabling the workshop to formulate a more effective and targeted service quality improvement strategy.
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