PENERAPAN ALGORITMA C4.5 UNTUK MEMPREDIKSI KREDIT MACET PADA SISTEM PINJAMAN DIGITAL DI INDUSTRI FINTECH
Penerapan Algoritma C4.5 untuk Memprediksi Kredit Macet pada Sistem Pinjaman Digital di Industri FinTech
DOI:
https://doi.org/10.51903/e-bisnis.v17i2.2105Keywords:
Algoritma C4.5, kredit macet, FinTech, pohon keputusan, prediksi risiko.Abstract
This research aims to implement the C4.5 algorithm in predicting bad credit in digital loan systems in the FinTech industry. The C4.5 algorithm was chosen because of its ability to handle numeric and categorical attributes, as well as produce a decision tree that can be interpreted easily. This research uses a dataset containing customer transaction and profile information, such as employment status, income and payment history. Test results show that the C4.5 algorithm is able to achieve an accuracy of 89.6% in predicting the possibility of bad credit, so it can help FinTech companies manage credit risk more effectively.
References
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Xie, Y., et al. (2013). Credit Risk Evaluation Using Decision Trees and Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics.
Tsai, C.F., & Wu, J.W. (2008). Using Neural Networks and Decision Trees for Credit Risk Evaluation. Expert Systems with Applications, 35(4).
Zhang, H., et al. (2018). P2P Lending: Using Machine Learning to Predict Credit Risk. Journal of FinTech Applications.
Awan, A.G., & Sarwar, G. (2019). Machine Learning Techniques in FinTech: Applications in Credit Scoring. Journal of FinTech Applications.
Siddiqi, N. (2017). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons.
Lee, I., & Shin, Y.J. (2018). Fintech: Ecosystem, Business Models, Investment Decisions, and Challenges. Business Horizons.
Sun, H., et al. (2015). Credit Risk Prediction Model Using Machine Learning in FinTech. Journal of Financial Risk Management.
Witten, I.H., Frank, E., & Hall, M.A. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann.
Xie, Y., et al. (2013). Credit Risk Evaluation Using Decision Trees and Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics.
Tsai, C.F., & Wu, J.W. (2008). Using Neural Networks and Decision Trees for Credit Risk Evaluation. Expert Systems with Applications, 35(4).
Zhang, H., et al. (2018). P2P Lending: Using Machine Learning to Predict Credit Risk. Journal of FinTech Applications.
Awan, A.G., & Sarwar, G. (2019). Machine Learning Techniques in FinTech: Applications in Credit Scoring. Journal of FinTech Applications.
Siddiqi, N. (2017). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons.
Lee, I., & Shin, Y.J. (2018). Fintech: Ecosystem, Business Models, Investment Decisions, and Challenges. Business Horizons.
Sun, H., et al. (2015). Credit Risk Prediction Model Using Machine Learning in FinTech. Journal of Financial Risk Management.
Witten, I.H., Frank, E., & Hall, M.A. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
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Published
2024-12-03
How to Cite
Jarot Dian, J. D. S., Setiyo Prihatmoko, & Febryantahanuji Febryantahanuji. (2024). PENERAPAN ALGORITMA C4.5 UNTUK MEMPREDIKSI KREDIT MACET PADA SISTEM PINJAMAN DIGITAL DI INDUSTRI FINTECH: Penerapan Algoritma C4.5 untuk Memprediksi Kredit Macet pada Sistem Pinjaman Digital di Industri FinTech. E-BISNIS: JURNAL ILMIAH EKONOMI DAN BISNIS, 17(2), 330–341. https://doi.org/10.51903/e-bisnis.v17i2.2105
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