Rapid technological developments encourage the banking sector to continue to innovate so as not to be left behind. Tight competition in this industry is caused by customers' freedom to choose products and services that are considered more profitable. This phenomenon is known as Customer Churn, which is a condition where customers choose not to continue subscribing to a particular company. The method applied uses a machine learning approach and customer segmentation approach. The churn analysis results show that the machine learning model, especially the random forest model, has the highest level of accuracy with an F1-Score of 91%. This model has the potential to reduce churn rates from 20.4% to 5.61%, illustrating its positive impact. Apart from that, for the clustering results, the K-Prototype model was obtained for the clustering model with the highest Silhouette Score number of 0.1557 and 4 clusters were obtained.