Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification
DOI:
https://doi.org/10.32736/sisfokom.v14i1.2317Keywords:
Fingerprint, Optimization, Classification, VGG-16. CNNAbstract
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.References
E. Iriyanto and H. Halif, “UNSUR RENCANA DALAM TINDAK PIDANA PEMBUNUHAN BERENCANA,” Jurnal Yudisial, vol. 14, no. 1, p. 19, Apr. 2021, doi: 10.29123/jy.v14i1.402.
Triwani, “Pemeriksaan Dermatoglifi sebagai Alat Identifikasi dan Diagnostik”.
Agus Andreansyah, Rika Favoria Gusa, and Muhammad Jumnahdi, “Pengenalan Pola Sidik Jari Menggunakan MultiClass Support Vector Machine,” ELKHA, vol. 11, pp. 79–84, 2019.
Didiek Wahju Indarta, “Fungsi Sidik Jari Dalam Proses Mengidentifikasi Korban dan Pelaku di Kepolisian Resort Bojonegoro,” 2021.
A. Z. Hibatullaha, M. F. Rahmanb, and A. P. Saric, “Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah,” 2024. [Online]. Available: www.elektro.itn.ac.id
M. Daktiloskopi, A. Tantangan, and D. Harapan, “BADAN PENGEMBANGAN SUMBER DAYA MANUSIA HUKUM DAN HAK ASASI MANUSIA KEMENTERIAN HUKUM DAN HAK ASASI MANUSIA REPUBLIK INDONESIA 2020 MODUL BEST PRACTICE,” 2020.
H. Rizkyawan, D. Juardi, J. Haerul Jaman, S. Karawang Jl HSRonggo Waluyo, K. Telukjambe Tim, and K. Karawang, “ANALISIS DIGITAL IMAGE FORENSIK TERHADAP KEASLIAN FILE IMAGE MELALUI WHATSAPP DENGAN METODE NIST,” 2024.
C. Militello, L. Rundo, S. Vitabile, and V. Conti, “Fingerprint classification based on deep learning approaches: Experimental findings and comparisons,” Symmetry (Basel), vol. 13, no. 5, May 2021, doi: 10.3390/sym13050750.
Rohman Dijaya and Hamzah Setiawan, Buku Ajar Pengolahan Citra Digital. UMSIDA PRESS, 2023.
M. Hirsi Mohamed, “Fingerprint Classification Using Deep Convolutional Neural Network,” Journal of Electrical and Electronic Engineering, vol. 9, no. 5, p. 147, 2021, doi: 10.11648/j.jeee.20210905.11.
F. Nurona Cahya et al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN),” 2021. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
M. S. Liyananta, M. Shata, N. Latifah, F. Bimantoro, and T. Informatika, “Program Studi Teknik Informatika,” 2024. [Online]. Available: https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256
J. Wira and G. Putra, “Pengenalan Konsep Pembelajaran Mesin dan Deep Learning Edisi 1.4 (17 Agustus 2020),” 2020.
M. A. Satriawan and W. Widhiarso, “Klasifikasi Pengenalan Wajah Untuk Mengetahui Jenis Kelamin Menggunakan Metode Convolutional Neural Network,” Jurnal Algoritme, vol. 4, no. 1, pp. 43–52, 2023, doi: 10.35957/algoritme.xxxx.
U. Nur et al., “BIO EDUCATIO (The Journal of Science and Biology Education) Identifikasi Populasi Pola Sidik Jari di Lingkungan II, Kelurahan Tembung, Kecamatan Tembung,” vol. 8, no. 1, p. 69, 2023, doi: 10.31949/be.v6i2.3317.
S. Syarif and M. Baharuddin, “PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK PADA FACE RECOGNITION UNTUK SMART LOKER,” 2023.
R. Garg, G. Singh, A. Singh, and M. P. Singh, “Fingerprint recognition using convolution neural network with inversion and augmented techniques,” Systems and Soft Computing, vol. 6, Dec. 2024, doi: 10.1016/j.sasc.2024.200106.
D. L. Andreea-Monica, S. Moldovanu, and L. Moraru, “A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks,” Inventions, vol. 7, no. 2, Jun. 2022, doi: 10.3390/inventions7020039.
K. Azmi, S. Defit, and U. Putra Indonesia YPTK Padang Jl Raya Lubuk Begalung-Padang-Sumatera Barat, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat,” vol. 16, no. 1, p. 2023, 2023.
S. Trabelsi, D. Samai, F. Dornaika, A. Benlamoudi, K. Bensid, and A. Taleb-Ahmed, “Efficient palmprint biometric identification systems using deep learning and feature selection methods,” Neural Comput Appl, vol. 34, no. 14, pp. 12119–12141, Jul. 2022, doi: 10.1007/s00521-022-07098-4.
D. Sely Wita and D. Yanti Liliana, “Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network (CNN),” JURTI, vol. 6, no. 1, 2022, [Online]. Available: https://www.kaggle.com/mahdieizadpanah/birjand-university-
S. A. Suryaman, R. Magdalena, and S. Sa’idah, “Klasifikasi Cuaca Menggunakan Metode VGG-16, Principal Component Analysis Dan K-Nearest Neighbor,” Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 1, pp. 1–8, Aug. 2021, doi: 10.54082/jiki.1.
A. Prayoga, Maimunah, P. Sukmasetya, Muhammad Resa Arif Yudianto, and Rofi Abul Hasani, “Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta,” Journal of Applied Computer Science and Technology, vol. 4, no. 2, pp. 82–89, Nov. 2023, doi: 10.52158/jacost.v4i2.486.
D. Irfan, R. Rosnelly, M. Wahyuni, J. T. Samudra, and A. Rangga, “PERBANDINGAN OPTIMASI SGD, ADADELTA, DAN ADAM DALAM KLASIFIKASI HYDRANGEA MENGGUNAKAN CNN,” 2022. [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
R. A. Jasin and V. L. Santoso, “Pengembangan Arsitektur VGG16 dan DCNN7 pada Convolutional Neural Network dalam Melakukan Klasifikasi Pose Yoga,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 2, p. 314, Jul. 2023, doi: 10.26418/justin.v11i2.55533.
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