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American Sign Language (ASL) is a sign language used to communicate for deaf people. The method used to identify ASL is Convolutional Neural Network (CNN). The architecture used by LeNet and AlexNet. The results of each architecture are then compared. The research was conducted with 2 schemes of the amount of data used, namely the first scheme of 100 data per letter and the second scheme of 1,000 data per letter to test the performance of the two architectures. The research results after being tested with new data, the first scheme for the LeNet architecture produces an overall accuracy of 48.332% and the AlexNet architecture produces an overall accuracy of 32.584%. The second scheme for the LeNet architecture produces an overall accuracy of 92.468% and the AlexNet architecture produces an overall accuracy of 91.618%. Overall comparison can be said that the LeNet architecture is the best architecture in this study.


Klasifikasi ASL CNN LeNet AlexNet Classification ASL CNN Lenet AlexNet

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How to Cite
Al Rivan, M. E., & Riyadi, A. G. (2021). Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language. Jurnal Komputer Terapan , 7(1), 53–61.


  1. Shivashankara and Srinath, “American Sign Language Recognition System: An Optimal Approach,” Int. J. Image, Graph. Signal Process., vol. 10, no. 8, pp. 18–30, 2018, doi: 10.5815/ijigsp.2018.08.03.
  2. T. Hunt, J. Carper, T. Lasley, C. Raisch, and E. Drasgow, “American Sign Language,” Encycl. Educ. Reform Dissent, 2013, doi: 10.4135/9781412957403.n31.
  3. M. E. Al Rivan, H. Irsyad, K. Kevin, and A. T. Narta, “Pengenalan Alfabet American Sign Language Menggunakan K-Nearest Neighbors Dengan Ekstraksi Fitur Histogram Of Oriented Gradients,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 3, pp. 328–339, 2020, doi: 10.28932/jutisi.v5i3.1936.
  4. I. Fareza, R. Busdin, M. E. Al Rivan, and H. Irsyad, “Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching,” J. Tek. Inform. dan Sist. Inf., vol. 4, no. 1, pp. 82–92, 2018, doi: 10.28932/jutisi.v4i1.747.
  5. M. Ezar, A. Rivan, and M. T. Noviardy, “Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan,” vol. 6, pp. 442–451, 2020.
  6. M. Zufar and B. Setiyono, “Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-Time,” J. Sains dan Seni ITS, vol. 5, no. 2, p. 128862, 2016, doi: 10.12962/j23373520.v5i2.18854.
  7. M. Bagus, S. Bakti, and Y. M. Pranoto, “Pengenalan Angka Sistem Isyarat Bahasa Indonesia Dengan Menggunakan Metode Convolutional Neural Network,” pp. 11–16, 2019.
  8. M. Swapna, Y. K. Sharma, and B. M. G. Prasadh, “CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 953–960, 2020, doi: 10.35940/ijrte.f9532.038620.
  9. S. Kulkarni and S. Harnoorkar, “Comparative Analysis of CNN Architectures,” vol. 7, no. June 6, pp. 1459–1464, 2020.
  10. M. Kayed, A. Anter, and H. Mohamed, “Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture,” Proc. 2020 Int. Conf. Innov. Trends Commun. Comput. Eng. ITCE 2020, no. February, pp. 238–243, 2020, doi: 10.1109/ITCE48509.2020.9047776.
  11. S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018, doi: 10.32528/JUSTINDO.V3I2.2254.
  12. V. Maeda-Gutiérrez et al., “Comparison of convolutional neural network architectures for classification of tomato plant diseases,” Appl. Sci., vol. 10, no. 4, 2020, doi: 10.3390/app10041245.
  13. Akash, “ASL Alphabet,”, 2018.