Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns

Authors

  • Dodo Zaenal Abidin Magister of Information System, Faculty of Computer Science, Universitas Dinamika Bangsa, Jambi, Indonesia
  • Maria Rosario Information System, Faculty of Computer Science, Universitas Dinamika Bangsa, Jambi, Indonesia
  • Ali Sadikin Information System, Faculty of Computer Science, Universitas Dinamika Bangsa, Jambi, Indonesia
  • Nurhadi Information System, Faculty of Computer Science, Universitas Dinamika Bangsa, Jambi, Indonesia
  • Jasmir Department Computer Engineering, Faculty of Computer Science, Universitas Dinamika Bangsa, Jambi, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.3.4585

Keywords:

Support Vector Machine; SMOTE; bank marketing; hyperparameter optimization; predictive modeling.

Abstract

Identifying potential customers for term deposit products remains a challenge in the banking industry due to class imbalance in marketing datasets. This study proposes an integrated approach that combines Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter tuning via GridSearchCV to enhance prediction performance. The dataset comprises 45,211 records containing demographic and campaign-related features. Preprocessing steps include categorical encoding, feature scaling, and SMOTE-based resampling. The optimized SVM model achieves an accuracy of 91% and an AUC of 0.96, outperforming the baseline model and demonstrating strong discriminatory ability, particularly for the minority class. This method improves the balance between precision and recall while reducing bias toward the majority class. The findings confirm the effectiveness of combining SMOTE and SVM for imbalanced classification tasks in the financial domain. These results contribute to the advancement of applied machine learning in informatics, particularly in developing robust decision support systems for data-driven banking strategies. Future work may extend this approach to diverse datasets and explore advanced resampling or ensemble techniques to improve model generalization.

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Additional Files

Published

2025-06-23

How to Cite

[1]
D. Z. Abidin, M. Rosario, A. Sadikin, N. Nurhadi, and J. Jasmir, “Improving Term Deposit Customer Prediction Using Support Vector Machine with SMOTE and Hyperparameter Tuning in Bank Marketing Campaigns ”, J. Tek. Inform. (JUTIF), vol. 6, no. 3, pp. 1267–1278, Jun. 2025.