One of the classification algorithms are frequently used and got a lot of attention of researchers inÂ predicting the financing problems in banking is Clasifier Naive Bayes and Decision Tree (C4.5).Â But the disadvantages faced on both these algorithms is the duration and degree of predictionÂ accuracy which is used to make predictions. This issue is also of concern to many researchers to fixÂ it so that the performance time and the prediction accuracy is becoming shorter but performanceÂ remains good accuracy. Because of these conditions, the two algorithms are we going badingkan ofÂ rate Accuracy and performance prediction of time to improve performance. Step-by-step analysisÂ of the financing method Clasifier Naive Bayes is to count the number of class / label, count theÂ number of the same case with the same class, multiply all variable results, comparing the resultsÂ with the results of the largest class will be used as a decision. Method of Decision Tree (C4.5) isÂ one of the algorithms used to perform the classification or segmentation or clustering andÂ predictive. Step-by-step analysis of the financing with this method is to select the attributes as root and create a branch for each value continue to divide the case into a branch and repeat the process for each branch until all cases the branches have the same class. Results of analysis of both methods are compared to see the ease of use and accuracy of calculation of foreign respective algorithms. From calculations that have been in trials it shows that the method Decision Tree (C4.5) has a higher degree of accuracy and efficiency in a faster time than the method Naive Bayes classifier.Keywords: Financing, Classifier, Decision Tree, C4.5, Naive Bayes.
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