Abstract
Customer satisfaction has become an important aspect for every business in today's competitive market. Understanding customer needs, wants, and expectations is critical for a business to provide outstanding customer service and retain customers. Therefore, this research represents a comparative study between two machine learning algorithms, Decision Tree and K-Nearest Neighbor, to predict hotel customer satisfaction. This study aims to identify which algorithm is more effective in predicting customer satisfaction by evaluating their performance using various metrics. The methodology used includes data preprocessing, feature selection, and machine learning model creation. The results show that the Decision Tree algorithm is superior to the K-Nearest Neighbor in terms of accuracy and precision. The findings from this study provide insights for businesses in the hospitality industry on how to predict customer satisfaction and improve their services.
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