Comparison of Classification Methods using Historical Loan Application Data

Kelen, Yohanes R. Laberto and Emanuel, Andi Wahju Rahardjo (2019) Comparison of Classification Methods using Historical Loan Application Data. In: Proceedings 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). UNIVERSITAS AMIKOM YOGYAKARTA, Yogyakarta, Indonesia, pp. 1-4. ISBN 978-1-7281-5118-2

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Abstract

Every year, the number of cooperatives in the province of East Nusa Tenggara continues to grow. Cooperatives are present with the aim of helping the community on the financial side. The cooperative offers the principle of saving and providing low-interest loans to its members. But there are times when lending is subjective. This condition is a major factor in the occurrence of errors in providing credit that leads to congestion (non-performing loans). This study focuses on the comparison of five classification methods using historical loan application data for a Multipurpose Cooperative in East Nusa Tenggara. The 5 methods are Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) Random Forest, and C4.5. In the test results, it turns out that the C4.5 Method has better accuracy and a smaller error rate.

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