Abstract
In research conducted on the Neural Network classification model that has been tested has an accuracy of 82.04% with a classification error rate of 17.96%. Meanwhile, the Decision Tree classification model has an accuracy rate of 99.38 %% with a classification error rate of 0.62%. Based on the test results from the two classification models, it can be concluded that the success of the Decision Tree can be used as a reference to improve the performance of the classification model's accuracy compared to the Neural Network Backpropagation model.
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