Default Risk Prediction Using Decision Tree Study Case of Home Credit

Prediksi Risiko Default Menggunakan Decision Tree Studi Kasus Home Credit

  • Dewa Nyoman Agung Adipurwa Mahandiri Universitas Udayana
  • Agus Muliantara Universitas Udayana

Abstract

Consuming loans with any service has become a trend in modern society. However, that trend gives some risk for the loan company such as Home Credit. Home Credit needs to create an automation analytic for predicting customers that might be default in future. So, we build a machine learning model using the Decision Tree algorithm to resolve that risk. The Decision Tree model can give mean score 85% accuracy, 91% precision ,and 92% recall score for Home Credit study case.

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Published
2024-01-23
How to Cite
ADIPURWA MAHANDIRI, Dewa Nyoman Agung; MULIANTARA, Agus. Default Risk Prediction Using Decision Tree Study Case of Home Credit. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 12, n. 3, p. 647-654, jan. 2024. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/92619>. Date accessed: 30 may 2024. doi: https://doi.org/10.24843/JLK.2023.v12.i03.p19.

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