Compiler (Nov 2023)

Implementation of the Decission Tree Algorithm to Determine Credit Worthiness

  • Abdussomad Abdussomad,
  • Ilham Kurniawan,
  • Agung Wibowo

DOI
https://doi.org/10.28989/compiler.v12i2.1911
Journal volume & issue
Vol. 12, no. 2
pp. 103 – 108

Abstract

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Credit is a loan from a bank that needs to be repaid with interest. In practice, problematic credit or bad credit often occurs due to less thorough credit analysis in the credit granting process, or from bad customers. This research aims to predict creditworthiness using the Decision Tree Classification Algorithm and find a solution for determining it. This research uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. This research method tests the effects of using the decision tree, Support Vector Machine, and Naïve Bayes model with the Decision Tree Classification Algorithm. The decision tree classification algorithm accurately analyzed problem loans and non-problem debtors at 93.49%. The decision tree algorithm test results are better than the support vector machine by 3.45%, and naïve bayes by 13.03%. The results of our study were also 4.16% better than the previous study. This research has also implemented the selected model in the form of website application deployment.

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