<![CDATA[In the internet service industry, identifying and predicting VIP customers is crucial for enhancing retention and profitability. This study aims to predict VIP customers using the linear regression method. The data used includes various customer attributes such as monthly data usage, subscription duration, payment history, and bandwidth used. By applying linear regression, a model was developed to identify the factors that most influence the VIP status of customers. The results of the study show that monthly data usage and subscription duration are significant predictors for classifying VIP customers. The resulting linear regression model has an adequate level of accuracy in predicting VIP customers. These findings can help internet service providers design more effective marketing strategies and service personalization to enhance customer satisfaction and loyalty. The application of linear regression in VIP customer prediction provides valuable insights into customer behavior and enables companies to be proactive in managing customer relationships. This research also opens opportunities for further exploration using more complex analytical methods such as logistic regression and machine learning to improve prediction accuracy.]]>
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