<![CDATA[This study analyzes the potential for customer churn among Indibiz Telkom Regional I business customers by implementing the Decision Tree C4.5 algorithm. The primary issue addressed is the high risk of losing business customers, which impacts revenue stability. This research develops an automated classification system utilizing customer behavior attributes such as payment status, total tickets, and total complaints. The research methodology includes data collection, preprocessing, calculating entropy and information gain, and constructing a decision tree. The results reveal that the "total ticket" attribute has the highest gain ratio, indicating that the frequency of service disruptions is the most dominant factor in triggering churn. Testing of the developed web-based system demonstrated an accuracy rate of 68% in classifying customers into churn and non-churn categories. The implementation of the C4.5 algorithm proves effective in mapping customer behavior patterns and serves as a decision support instrument for management to determine more targeted customer retention strategies.]]>
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