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Submitted
8 February 2021
Accepted
31 May 2021
Published
1 June 2021
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Abstract

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.

Keywords

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. https://doi.org/10.35143/jkt.v7i1.4489
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