<![CDATA[The rapid growth of the telecommunications industry has increased competition among companies for customers. As a result, customers often switch services or terminate subscriptions. Retaining customers is very important as it is 10 times cheaper than acquiring new customers. This study compares Random Forest (RF) and Convolutional Neural Network (CNN) algorithms in predicting customer switching, using Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for data partitioning. Model evaluation using Confusion Matrix and Area Under Curve (AUC). The evaluation results show that the performance of CNN models with optimization parameters is superior. Using the CFS dataset, the test data evaluation results obtained an accuracy of 98%, AUC 0,96, precision 99%, recall 92%, and f1-score 96%. The best tuning result for CNN is 3 combinations of filter and kernel {[64&7], [32&3], [16&2]} and pool_size=2. The limitation of this research is how to handle the two algorithms being compared. Both use different approaches, namely Supervised Learning and Deep Learning.]]>
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