Comparison of Decision Tree, Naive Bayes and Random Forest Algorithm to get the Best Performance of Algorithm for Customer Credit Classification - Suryani, Indah (Jurnal Riset Informatika, 2024)
Credit is a potential income and the most significant business operation risk for a bank. Bad credit has become an ingrained problem in the banking world. Therefore, this research aims to classify customer data profiles who have the opportunity to be able to apply for a loan or not to reduce the risk of bad credit in the future by classifying using three commonly used data mining algorithms, namely the Decision Tree algorithm, Naïve Bayes and Random forest. The research was conducted using an experimental, descriptive method by testing the accuracy of the three methods to get the best performance. Based on the experiments' results, the accuracy performance with the confusion matrix was 73.20% for the Decision Tree algorithm, then the accuracy for the Naive Bayes algorithm was 74.4% and Random Forest was 77.4%. Meanwhile, performance evaluation is based on the Receiver Operating Characteristics (ROC) curve by looking at the resulting Area Under Curve (AUC) value of 0.717 for the Decision Tree algorithm, while Naive Bayes produces an AUC value of 0.741 and the largest is Random Forest at 0.796. So it can be concluded that the best performance of the classification carried out is the one that uses the Random Forest algorithm. Then, from the validation results using the T-Test of the three methods being compared, Random Forest produces a significant difference in the level of accuracy compared to the accuracy produced by the Decision Tree, namely with an alpha value of 0.031.