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Comparative Evaluation of Machine Learning Models for Credit Card Approval Prediction Using Class Imbalance Handling Methods in the PyCaret AutoML Framework

  • Mario Suryowisnu Wicaksono1*
  • Magdalena A. Ineke Pakereng2
1,2 Satya Wacana Christian University image/svg+xml
* Corresponding Author: Mario Suryowisnu Wicaksono

Abstract

Credit card approval processes are often performed manually, leading to inefficiencies, higher operational costs, and inconsistent decision-making. This study comparatively evaluates several machine learning models under different class imbalance handling strategies to predict credit card approval using the PyCaret AutoML framework. Two experimental scenarios were conducted on a dataset of 25,128 credit card applications, namely PyCaret's built-in SMOTE-based imbalance handling and random undersampling. In the SMOTE-based scenario, the Random Forest model achieved high accuracy, but its recall for the minority class was only 36.11%. In contrast, random undersampling produced more balanced performance, with the Gradient Boosting model achieving 100% recall. These findings indicate that class imbalance handling plays a critical role in credit card approval prediction, and that random undersampling provides more reliable minority-class detection than PyCaret's built-in SMOTE approach for the evaluated dataset.

Keywords

Credit Card Approval Prediction Machine Learning Class Imbalance PyCaret AutoML

How to Cite

Citation Format
Wicaksono, M. S., & Pakereng, M. A. I. (2027). Comparative Evaluation of Machine Learning Models for Credit Card Approval Prediction Using Class Imbalance Handling Methods in the PyCaret AutoML Framework. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 11(1), 268-278. https://doi.org/10.35870/jtik.v11i1.7884

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Author Biographies

Mario Suryowisnu Wicaksono, Satya Wacana Christian University

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.

Magdalena A. Ineke Pakereng, Satya Wacana Christian University

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.

License

Copyright (c) 2027 Mario Suryowisnu Wicaksono, Magdalena A. Ineke Pakereng

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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