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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Araz, Rezty Amalia
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Computer Science & IT

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Lightweight Deep Learning Approach Using 1D-CNN and Attention for Sequential Credit Card Fraud Detection Nugroho H, Yabes Dwi; Rahmawati, Aulia; Araz, Rezty Amalia; Nuryono, Aninditya Anggari
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.115

Abstract

Fraudulent activity in credit card transactions continues to be a pressing concern in the financial industry, primarily because transaction data is highly complex and heavily skewed toward legitimate cases. To address this issue, the present study proposes a hybrid deep learning framework that merges the strengths of a one-dimensional convolutional neural network (1D-CNN) with the selective capabilities of an attention mechanism. The performance of this enhanced model was rigorously compared with a conventional 1D-CNN, employing widely recognized evaluation metrics such as accuracy, precision, recall, and the F1-score. The experimental outcomes demonstrate that introducing the attention layer substantially improves the network’s ability to recognize critical temporal dependencies in transaction sequences. As a result, the model achieved exceptional performance levels, with an accuracy of 98%, precision of 97%, recall of 98%, and an F1-score of 98%. These findings provide strong evidence of the superiority of the attention-based approach, highlighting its effectiveness in producing more reliable and resilient fraud detection systems. Beyond the algorithmic gains, the research contributes a practical foundation for real-time applications in financial security, enabling institutions to curtail potential losses, reinforce public confidence in digital payment services, and enhance the efficiency of day-to-day operations.
Co-Authors Aulia Rahmawati Nugroho H, Yabes Dwi Nuryono, Aninditya Anggari