Jurnal Komputer Terapan
Vol. 7 No. 1 (2021): Jurnal Komputer Terapan

Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language

Muhammad Ezar Al Rivan (Teknik Informatika, Universitas Multi Data Palembang)
Alwyn Giovri Riyadi (Teknik Informatika, Universitas Multi Data Palembang)



Article Info

Publish Date
01 Jun 2021

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.

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Journal Info

Abbrev

jkt

Publisher

Subject

Computer Science & IT

Description

Applied Computer Journal Articles from various fields in Informatics, Information Systems and Computer science. Topics included, 1. Informatics 1.1 Software Engineering 1.2 Multimedia 2. Information Systems 2.1 Soft Computing 2.2 Business Analyst 2.3 Data Engineering 3. Computer science 3.1 ...