Cerebrovascular accidents or strokes are one of the leading causes of death worldwide. Stroke is a disease caused by a malfunction of the blood vessels that supply the blood to the brain. Machine learning is a technology that can be used to predict stroke. Machine learning algorithms are constructive when making accurate predictions and providing accurate analysis. One of the machine learning classification algorithms that can be used for prediction is the Decision Tree C4.5 algorithm and the Naive Bayes algorithm. The purpose of this study is to compare the accuracy and performance of the two algorithms for predicting cerebrovascular disease or stroke. Based on the results of the study, it was found that the C4.5 algorithm achieved a higher accuracy of 95% and the Naive Bayes algorithm achieved a precision of 91%.