Optimization Artificial Neural Network (ANN) Models with Adam Optimizer to Improve Customer Satisfaction Business Banking Prediction

Yahya Nur Ifriza; Yusuf Wisnu Mandaya; Ratna Nur Mustika Sanusi; Hendra Febriyanto; Abdul Jabbar; Azlina Kamaruddin

doi:10.52436/1.jutif.2025.6.3.4776

Authors

Yahya Nur Ifriza Information Systems, Universitas Negeri Semarang, Indonesia
Yusuf Wisnu Mandaya Information Systems, Universitas Negeri Semarang, Indonesia
Ratna Nur Mustika Sanusi Mathematics, Universitas Negeri Semarang, Indonesia
Hendra Febriyanto Natural Science Education, Universitas Negeri Semarang, Indonesia
Abdul Jabbar Environmental Science, Universitas Negeri Semarang, Indonesia
Azlina Kamaruddin Computer Science, Universiti Teknologi Petronas, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.3.4776

Keywords:

Adam optimizer, Artificial neural network, Customer satisfaction, Optimization, Predictive modeling

Abstract

Customer satisfaction prediction is critical for business banking to retain clients and optimize services, yet existing models struggle with imbalanced data and suboptimal convergence. Traditional approaches lack adaptive learning mechanisms, limiting accuracy in real-world applications. This study developed an optimized Artificial Neural Network (ANN) model using the Adam algorithm to improve prediction accuracy for banking customer satisfaction. We trained an ANN on the Santander Customer Satisfaction Dataset (76,019 entries, 371 features) with Adam optimization. Preprocessing included normalization, removal of quasi-constant features, and an 80-20 train-test split. Adam’s adaptive learning rates and momentum were leveraged to address gradient instability. The model achieved 95.82% accuracy, 99.99% precision, 95.83% recall, a 97.87% F1-score, and 0.82 AUC, outperforming traditional optimizers like SGD. Training loss reduced by 30% with faster convergence. This work demonstrates Adam’s efficacy in handling imbalanced banking data, providing a scalable framework for customer analytics. The results advance computer science applications in fintech by integrating adaptive optimization with deep learning for high-stakes decision-making. This research contributes to the growing body of knowledge in machine learning applications for business analytics and provides a valuable framework for improving customer satisfaction prediction models in various industries and the advancement of deep learning applications in business intelligence, particularly in banking service quality prediction.

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