Optimalisasi Model Jaringan Syaraf Untuk Pengenalan CAPTCHA dengan Metode LeNet-5
DOI:
https://doi.org/10.47065/jimat.v5i3.476Keywords:
EMNIST; CAPTCHA; CNN; LeNet-5Abstract
In general, CAPTCHA is an image containing distorted letters or numbers. This test involves users typing the results of guessing letters or numbers in the distorted image as security before users can enter or access a website they want to go to. In this paper, a simulation of an automatic reader system for CAPTCHA has been created where the letters and numbers in the image are carried out in several stages. The initial stage involves training using the EMNIST dataset to train the model to recognize general letter and digit characters before recognition in the CAPTCHA image. Furthermore, the process of recognizing letters and numbers in different CAPTCHA images is carried out to read the text contained in the CAPTCHA. The Convolutional Neural Network (CNN) model of the LeNet-5 method is used as a method for reading distorted letters and numbers in CAPTCHA with a high level of accuracy, achieving 88.56%.
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G. Satya Nugraha, G. Pasek, S. Wijaya, F. Bimantoro, Y. Husodo, and F. Hamami, “INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage?: www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Arabic Character Recognition Using CNN LeNet-5.” [Online]. Available: www.joiv.org/index.php/joiv
J. Zhang, X. Yu, X. Lei, and C. Wu, “A Novel Deep LeNet-5 Convolutional Neural Network Model for Image Recognition,” Computer Science and Information Systems, vol. 19, no. 3, pp. 1463–1480, Sep. 2022, doi: 10.2298/CSIS220120036Z.
M. Rafly Alwanda, R. Putra, K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” 2020.
A. H. Suherman, N. Ibrahim, H. Syahrian, V. P. Rahadi, and M. K. Prayoga, “KLASIFIKASI DAUN TEH GAMBUNG VARIETAS ASSAMICA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR LENET-5,” JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING, vol. 4, no. 2, pp. 63–71, Feb. 2021, doi: 10.31289/jesce.v4i2.4136.
M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.
R. Bangun Pengaman Sistem Login Menggunakan Metode Captcha and D. Rusmana, “Rancang Bangun-Dadang Rusmana DESIGN AND BUILD LOGIN SYSTEM SECURITY USING CAPTCHA METHOD,” vol. 10, no. 1, 2021.
D. Sutaji and D. N. Husenti, “DETEKSI KARAKTER PADA CITRA CAPTCHA LOGIN INTERNET BANKING MENGGUNAKAN TEMPLATE MATCHING,” 2019.
M. A. Yasin, “MEMECAHKAN CAPTCHA-TEXT TERDISTORSI DENGAN CONVOLUTIONAL NEURAL NETWORK,” 2024.
A. Mulyanto, E. Susanti, F. Rossi, W. Wajiran, and R. I. Borman, “Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR),” J. Edukasi dan Penelit. Inform., vol. 7, no. 1, p. 52, 2021, doi: 10.26418/jp.v7i1.44133.
F. Ilham and N. Rochmawati, “Transliterasi Aksara Jawa Tulisan Tangan ke Tulisan Latin Menggunakan CNN,” J. Informatics Comput. Sci., vol. 1, no. 04, pp. 200–208, 2020, doi: 10.26740/jinacs.v1n04.p200-208.
Y. Brianorman and R. Munir, “Perbandingan Pre-Trained CNN: Klasifikasi Pengenalan Bahasa Isyarat Huruf Hijaiyah,” J. Sist. Info. Bisnis, vol. 13, no. 1, pp. 52–59, 2023, doi: 10.21456/vol13iss1pp52-59.
F. Nuraeni et al., “IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK ( CNN ) UNTUK PENGENALAN BACAAN TAJWID BERDASARKAN GAMBAR TULISAN DALAM AL- QUR ’ AN,” vol. 8, no. 5, pp. 11018–11025, 2024.
M. Malika and E. Widodo, “Implementasi Deep Learning Untuk Klasifikasi Gambar Menggunakan Convolutional Neural Network (Cnn) Pada Batik Sasambo,” Pattimura Proceeding Conf. Sci. Technol., pp. 335–340, 2022, doi: 10.30598/pattimurasci.2021.knmxx.335-340.
N. Fadlia and R. Kosasih, “Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn),” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 207–215, 2019, doi: 10.35760/tr.2019.v24i3.2397.
D. Dhelviana, T. Amelia, J. Sulaksono, and D. W. Widodo, “Sistem Pendeteksi Kekerasan Berbasis Cnn ( Convolutional Neural Network ),” vol. 2, pp. 457–462, 2023.
S. Prihatiningsih, N. S. M, F. Andriani, and N. Nugraha, “Analisa Performa Pengenalan Tulisan Tangan Angka Berdasarkan Jumlah Iterasi Menggunakan Metode Convolutional Neural Network,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 1, pp. 58–66, 2019, doi: 10.35760/tr.2019.v24i1.1934.
R. N. Wicaksono, H. Nugroho, and G. E. Yuliastuti, “Pengenalan Pola Citra Ekspresi Wajah Manusia Menggunakan Masker Dengan Metode Convolutional Neural Network (CNN),” Pros. Semin. Nas. Sains dan Teknol. Terap., pp. 1–6, 2023, [Online]. Available: http://ejurnal.itats.ac.id/sntekpan/article/view/5157%0Ahttp://ejurnal.itats.ac.id/sntekpan/article/download/5157/3571
A. Fadhila, M. Mabe Parenreng, J. Teknik Elektro, and P. Negeri Ujung Pandang, “Pengenalan Tanaman Herbal Daun Merica dan Daun Sirih Menggunakan Metode Convolutional Neural Network (CNN),” Pros. Semin. Nas. Tek. Elektro dan Inform., vol. 9, no. 1, pp. 109–113, 2023.
I. N. Pratama, T. Rohana, and T. Al Mudzakir, “Pengenalan Sampah Plastik Dengan Model,” no. Ciastech, pp. 691–698, 2020.
S. N. Amartama, A. N. Hidayah, P. K. Sari, and R. A. Ramadhani, “Implementasi Convolutional Neural Network (CNN) dalam Pengenalan Pola Penulisan Tangan,” Semin. Nas. Teknol. Sains, vol. 3, no. 1, pp. 133–138, 2024, doi: 10.29407/stains.v3i1.4155.
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