ALGORITMA KLASIFIKASI NAIVE BAYES UNTUK MENILAI KELAYAKAN KREDIT

CIPTOHARTONO, CLAUDIA CLARENTIA (2014) ALGORITMA KLASIFIKASI NAIVE BAYES UNTUK MENILAI KELAYAKAN KREDIT. Skripsi, Fakultas Ilmu Komputer.

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Abstract

Credit is a way of selling goods or loans money with no cash payment where the payment is deferred or repaid with a certain amount of the loan to the limit permitted by the bank or other entity. Along with the advancement of information technology, it is possible for companies to use statistical models to evaluate credits. Credit scoring models are built using samples of past credit in large numbers. Data mining has been proven as a tool that plays an important role for banking and retail industries, which identify useful information from a large size data. This study uses Naive Bayes model, this model holds the assumption that the relationship between the features or attributes are independent, which makes it simple and efficient. The result of this study proves that Naive Bayes algorithm can be applied to assess the credit of the BCA Finance Jakarta. And that the preprocessing is a step that greatly affects the final result to get an Excellent category for its accuracy. The credit assessment accuracy on BCA Finance Jakarta using initial data preprocessing is 85.57%, while after the initial data processing and preprocessing is 92.53%. Credit assessment using Naive Bayes algorithm on BCA Finance Jakarta is superior to after preprocessed initial data though Naive Bayes algorithm is capable of handling missing data.

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