COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND NAÏVE BAYES METHODS FOR CREDIT APPROVAL CLASSIFICATION (CASE STUDY: PT. ADIRA FINANCE TERNATE CITY)
DOI:
https://doi.org/10.33387/jati.v2i2.115Keywords:
data mining, Classification, credit risk, neural network, naive bayesAbstract
Credit is one of the shortcuts sometimes taken by people to fulfill their needs, but it carries significant risks for companies, such as defaulting credit customers. Data mining classification was chosen to help provide a solution to this problem. In this research, classification using the Neural Network and Naïve Bayes algorithm models was employed and compared to determine which algorithm model had the best accuracy in classifying the credit status of customers using the Python programming language. The evaluation was then conducted using a confusion matrix. The results of the Neural Network algorithm, after testing with the confusion matrix, showed the highest accuracy value at 85.0%, while the Naïve Bayes algorithm, after testing with the confusion matrix, showed the highest accuracy value at 84.7%. Based on these results, it can be concluded that the Neural Network performs slightly better in classifying credit customer data compared to Naïve Bayes.
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