Comparison of Gabor Filter Parameter Characteristics for Dorsal Hand Vein Authentication Using Artificial Neural Networks
(1) Program Studi Teknik Elektro, Universitas Nusa Putra
(2) Program Studi Teknik Elektro, Universitas Nusa Putra
(3) Program Studi Teknik Informatika, Universitas Nusa Putra
(*) Corresponding Author
Abstract
The importance of digital security in today's technological era requires various innovations in creating a reliable security system for humans. Biometrics is an authentication method and the most effective system for performing personal recognition because biometrics have unique characteristics. Dorsal hand vein become biometrics for the individual recognition process in this study using feature extraction of gabor filters and neural network backpropagation to classify recognition into five classes of human individuals, which are expected to be able to provide a higher accuracy value when compared to research on the introduction of dorsal hand vein. This classification process has several stages, namely input image, image pre-processing, segmentation, feature extraction, and image classification. The test results show that the percentage of success based on the five test scenarios has an average value of 75%. In this study, the results of the greatest test accuracy in the fourth scenario were 91%.
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I. Junaedi, “Pengembangan Teknologi Informasi Berbasis Access Id Card,” J. Inf. Syst. Informatics …, vol. 1, no. 1, 2017.
M. Arsal, bheta agus Wardijono, and D. Anggraini, “Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN,” J. Nas. Teknol. dan Sist. Inf., vol. 6, no. 1, pp. 55–63, 2020, doi: 10.1109/UBMK52708.2021.9559031.
A. Andreansyah, R. F. Gusa, and M. Jumnahdi, “Pengenalan Pola Sidik Jari Menggunakan Multi-Class Support Vector Machine,” J. ELKHA, vol. 11, no. 2, pp. 79–84, 2019.
F. E. Alfian, I. G. P. S. Wijaya, and F. Bimantoro, “Identifikasi Iris Mata Menggunakan Metode Wavelet Daubechies dan K-Nearest Neighbor,” J. Teknol. Informasi, Komputer, dan Apl. (JTIKA ), vol. 2, no. 1, pp. 1–10, 2020, doi: 10.29303/jtika.v2i1.76.
R. P. Saidah, Sofia, B. Novria, Aulia, G. Shekinah, and F. Wahid, “Analisis Perbandingan Metode LBP dan CLBP pada Sistem Pengenalan Individu Melalui Iris Mata,” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 6, no. 3, pp. 285–290, 2020.
D. S. Wita and D. Y. Liliana, “Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network ( CNN ),” J. Rekayasa Teknol. Inf., vol. 6, no. 1, pp. 1–7, 2022.
C. Susim, Theresia dan Darujati, “Pengolahan Citra Untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV,” J. Syntax Admiration, vol. 2, no. 3, pp. 534–545, 2021.
M. N. Ikhsan and R. Rahmadewi, “Sistem Keamanan Sepeda Motor Dengan Teknologi Biometrik Sidik Jari Menggunakan Sensor Fingerprint,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 7, no. 2, pp. 144–153, 2022.
M. S. Purba, “Perancangan Sistem Identifikasi Biometrik Iris Mata Menggunakan Metode Transformasi Hough,” vol. 7, no. 2, pp. 117–122, 2020.
H. Setiawan, “Telapak Tangan Menggunakan Learning Vector Quantization Palm Vein Image Identification Using Learning,” 2016.
K. Kim, H. W. Jeong, and Y. Lee, “Performance evaluation of dorsal vein network of hand imaging using relative total variation-based regularization for smoothing technique in a miniaturized vein imaging system: A pilot study,” Int. J. Environ. Res. Public Health, vol. 18, no. 4, pp. 1–12, 2021, doi: 10.3390/ijerph18041548.
M. Simanjuntak, K. Abdi Sinuraya, Irhamna, and J. Panjaitan, “Analisis simulasi dan evaluasi teknik pengenalan tanda tangan menggunakak rbf dan knn,” JUTISAL J. Tek. Inform. Univers., vol. 2, no. 1, pp. 54–60, 2022.
M. S. Simanjuntak and J. Panjaitan, “Sistem Information Retrieval Menggunakan K- Nearest Neighbour Dalam Klasifikasi Jurnal Bahasa Inggris,” JUTISAL J. Tek. Inform. Univers., vol. 1, no. 2, pp. 1–8, 2021.
K. M. Alashik and R. Yildirim, “Human Identity Verification from Biometric Dorsal Hand Vein Images Using the DL-GAN Method,” IEEE Access, vol. 9, pp. 74194–74208, 2021, doi: 10.1109/ACCESS.2021.3076756.
S. Bantun, J. Y. Sari, N. Z, M. Mardianto, and A. Achban, “Sistem Absensi Mahasiswa Berbasis Dorsal Hand Vein Menggunakan Local Binary Patterns dan Fuzzy k-NN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 384–396, 2022, doi: 10.35957/jatisi.v9i1.1496.
N. Fajriani, “Pengenalan Pola Garis Telapak Tangan Menggunakan Metode Fuzzy K-Nearest Neighbor,” Edutic - Sci. J. Informatics Educ., vol. 4, no. 1, 2017, doi: 10.21107/edutic.v4i1.3385.
P. Nurtanto Andono, T. Sutojo, and Muljono, Pengolahan Citra Digital, 1st ed. Yogyakarta: Penerbit Andi, 2018.
A. Kadir and A. Susanto, Teori dan Aplikasi Pengolahan Citra, 1st ed. Yogyakarta: Penerbit Andi, 2013.
I. Setiawan, W. Dewanta, H. A. Nugroho, and H. Supriyono, “Pengolah Citra Dengan Metode Thresholding Dengan Matlab R2014A,” J. Media Infotama, vol. 15, no. 2, 2019, doi: 10.37676/jmi.v15i2.868.
A. Susanto, “Penerapan Operasi Morfologi Matematika Citra Digital Untuk Ekstraksi Area Plat Nomor Kendaraan Bermotor,” Pseudocode, vol. 6, no. 1, pp. 49–57, 2019, doi: 10.33369/pseudocode.6.1.49-57.
D. N. Rahmah, H. Tjandrasa, and A. Yuniarti, “Implementasi Segmentasi Pembuluh Darah Retina Pada Citra Morfologi Adaptif,” no. September 2016, pp. 1–6, 2011.
M. A. S. Yudono, R. R. Isnanto, and A. Triwiyatno, “Comparison of Cataract Classification System Based on Retinal Blood Vessels Objects and Retinal Optic Disc Using Backpropagation Neural Network,” Int. J. Innov. Eng. Technol., vol. 18, no. 2, pp. 1–8, 2021, doi: 10.13140/RG.2.2.16638.46408.
admi syarif, A. R. TANJUNG, R. ANDRIAN, and F. R. LUMBANRAJA, “Implementasi Metode Ekstraksi Fitur Gabor Filter dan Probablity Neural Network (PNN) untuk Identifikasi Kain Tapis Lampung,” J. Komputasi, vol. 8, no. 2, 2020, doi: 10.23960/komputasi.v8i2.2641.
T. Wulandari and N. H. Waryanto, “Identifikasi Iris Mata dengan menggunakan Metode Hidden Markov Model dan Tapis Gabor Wavelet,” J. Kaji. dan Terap. Mat., vol. 7, no. 4, pp. 10–11, 2018.
R. N. Hidayat, R. R. Isnanto, and O. D. Nurhayati, “Implementasi Jaringan Syaraf Tiruan Perambatan Balik untuk Memprediksi Harga Logam Mulia Emas Menggunakan Algoritma Lavenberg Marquardt,” J. Teknol. dan Sist. Komput., vol. 1, no. 2, p. 49, 2013, doi: 10.14710/jtsiskom.1.2.2013.49-55.
S. Kusumadewi, Membangung Jaringan Syaraf Tiruan Menggunakan Matlab & Excel link. Yogyakarta: Graha Ilmu, 2004.
G. Ramadhona, B. D. Setiawan, and F. A. Bachtiar, “Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 12, pp. 6048–6057, 2018.
G. Z. Muflih, S. Sunardi, and A. Yudhana, “Jaringan Saraf Tiruan Backpropagation untuk Prediksi Curah Hujan di Wilayah Kabupaten Wonosobo,” MUST J. Math. Educ. Sci. Technol., vol. 4, no. 1, p. 45, 2019, doi: 10.30651/must.v4i1.2670.
R. Riyanda, A. H. H. Pardede, and R. Saragih, “Jaringan Syaraf Tiruan Memprediksi Kebutuhan Obat-Obatan Menggunakan Metode Backpropagation ( Studi Kasus : UPTD Puskesmas Bahorok ),” Semin. Nas. Inform., pp. 47–55, 2021.
M. E. Al Rivan and T. Juangkara, “Identifikasi Potensi Glaukoma dan Diabetes Retinopati Melalui Citra Fundus Menggunakan Jaringan Syaraf Tiruan,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 1, pp. 43–48, 2019, doi: 10.35957/jatisi.v6i1.158.
F. Pontoh, H. V. F. Kainde, and Y. V. Akay, “Teknik pengenalan pembuluh darah punggung tangan berbasis fitur local binary pattern,” J. Widya, vol. 2, no. 2, pp. 198–203, 2021, doi: 10.54593/awl.v2i2.15.
DOI: https://doi.org/10.32736/sisfokom.v12i3.1819
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