Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification
Fingerprint is a unique biometric identity commonly used as evidence in court. However, its quality can decline due to external factors such as uneven surfaces, weather conditions, or distortion. The dataset used in this study is FVC2000. Convolutional Neural Networks (CNN) were applied for fingerpr...
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LPPM ISB Atma Luhur
2025-01-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2317 |
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| author | Agus Andreansyah Julian Supardi |
| author_facet | Agus Andreansyah Julian Supardi |
| author_sort | Agus Andreansyah |
| collection | DOAJ |
| description | Fingerprint is a unique biometric identity commonly used as evidence in court. However, its quality can decline due to external factors such as uneven surfaces, weather conditions, or distortion. The dataset used in this study is FVC2000. Convolutional Neural Networks (CNN) were applied for fingerprint image enhancement and classification, focusing on patterns such as whorl, arch, radial loop, ulnar loop, and twinted loop. This research optimized the VGG-16 model by adding several hyperparameters. The results showed the highest accuracy of 100% on the testing data with a learning rate of 0.0001, using 50 epochs and a training-to-validation data split ratio of 80%:10% from a total of 400 fingerprint image pattern data. These findings demonstrate that the VGG-16 model successfully classified fingerprint images with optimal performance, contributing significantly to the development of CNN-based fingerprint classification systems. |
| format | Article |
| id | doaj-art-a8843279ca344373ba9e102c2e6492c1 |
| institution | Kabale University |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-a8843279ca344373ba9e102c2e6492c12025-02-12T07:27:38ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-01-01141424810.32736/sisfokom.v14i1.23171980Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager ClassificationAgus Andreansyah0Julian Supardi1Department of Masters in Computer Science, University of SriwijayaDepartment of Master of Computer Science, University of SriwijayaFingerprint is a unique biometric identity commonly used as evidence in court. However, its quality can decline due to external factors such as uneven surfaces, weather conditions, or distortion. The dataset used in this study is FVC2000. Convolutional Neural Networks (CNN) were applied for fingerprint image enhancement and classification, focusing on patterns such as whorl, arch, radial loop, ulnar loop, and twinted loop. This research optimized the VGG-16 model by adding several hyperparameters. The results showed the highest accuracy of 100% on the testing data with a learning rate of 0.0001, using 50 epochs and a training-to-validation data split ratio of 80%:10% from a total of 400 fingerprint image pattern data. These findings demonstrate that the VGG-16 model successfully classified fingerprint images with optimal performance, contributing significantly to the development of CNN-based fingerprint classification systems.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2317fingerprintoptimizationclassificationvgg-16. cnn |
| spellingShingle | Agus Andreansyah Julian Supardi Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification Jurnal Sisfokom fingerprint optimization classification vgg-16. cnn |
| title | Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification |
| title_full | Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification |
| title_fullStr | Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification |
| title_full_unstemmed | Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification |
| title_short | Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification |
| title_sort | optimization of vgg 16 accuracy for fingerprint pattern imager classification |
| topic | fingerprint optimization classification vgg-16. cnn |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2317 |
| work_keys_str_mv | AT agusandreansyah optimizationofvgg16accuracyforfingerprintpatternimagerclassification AT juliansupardi optimizationofvgg16accuracyforfingerprintpatternimagerclassification |