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All Journal EKSAKTA: Journal of Sciences and Data Analysis
Alifia Tanza
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology

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Comparison of the Naïve Bayes Classifier and Decision Tree J48 for Credit Classification of Bank Customers Alifia Tanza; Dina Tri Utari
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art2

Abstract

The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.
Co-Authors Dina Tri Utari