IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES

Penulis

  • Rizki Hesananda Universitas Siber Indonesia
  • Vian Firmansyah Bank Rakyat Indonesia

DOI:

https://doi.org/10.33480/inti.v19i2.6155

Kata Kunci:

debit card classification, deep learning, object detection, YOLOv5

Abstrak

This study aims to develop an object detection model based on YOLOv5 to classify debit card types. With the advancement of financial technology, the need for automated systems to identify debit cards has become essential to enhance transaction efficiency and security. The research methodology involves five main stages: dataset collection, data preprocessing through labeling and resizing to 640 x 640, dataset augmentation, YOLOv5 model training, and model evaluation. The dataset used consists of three categories of debit cards, with a total of 300 images. The results demonstrate that the YOLOv5 model achieves excellent performance with a mean average precision (mAP) of 92.7% and an object loss value of 0.08. The high mAP value indicates the model’s capability to accurately recognize objects, while the low object loss value reflects minimal detection errors during testing. In conclusion, YOLOv5 has proven to be reliable for application in debit card detection systems. This study provides significant contributions to the development of automation systems in the financial sector, particularly in improving the efficiency and accuracy of identification processes. It is hoped that this research will serve as a foundation for further studies with broader datasets, the application of more advanced augmentation techniques, and the utilization of more sophisticated hardware to enhance model performance.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

Rizki Hesananda, Universitas Siber Indonesia

Hi, my name is Hesa. I am a practitioner of the IT industry who is also active in teaching, research and community service as a computer science lecturer. I work as a Web Developer. I have done several types of work such as freelance, corporate companies, ministries and start-ups. I have come to understand, different types of organizations, different needs and their approach to IT needs.

Early in my career, I started a career in the IT industry to apply the computer science I learned in college. I want to know what the real world is like as an IT worker. Not that I'm an expert, but that the industrial world is far more sophisticated than I imagined. I am more and more interested in exploring this field. Then I decided to take my Masters and took some online courses on Computer Science. I work on more than 50 websites, whether it's done in a team or alone, both successful and unsuccessful.

I am a teacher and a learner. At this time, I want to share the knowledge that I got in my master's degree course and my experience from practicing in the IT industry. What I understand is that teaching is the most effective way to learn compared to just reading or taking notes. As a lecturer, I think it is very necessary to understand new fields and always be updated about the outside world. Therefore, now I am starting to explore the fields of Artificial Intelligence such as Data Mining and Computer Vision.

As far as I know, Science and practice in the IT world is developing at an exponential rate. Therefore, the ability to work in teams, adapt to the environment and habits to increase self- capacity are very essential skills.

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Diterbitkan

2025-02-04

Cara Mengutip

Hesananda, R., & Firmansyah, V. (2025). IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES. INTI Nusa Mandiri, 19(2), 164–171. https://doi.org/10.33480/inti.v19i2.6155

Terbitan

Vol 19 No 2 (2025): INTI Periode Februari 2025

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Articles

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Hak Cipta (c) 2025 Rizki Hesananda, Vian Firmansyah

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