Alwyn Giovri Riyadi
Teknik Informatika, Universitas Multi Data Palembang

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language Muhammad Ezar Al Rivan; Alwyn Giovri Riyadi
Jurnal Komputer TerapanĀ  Vol. 7 No. 1 (2021): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (235.304 KB) | DOI: 10.35143/jkt.v7i1.4489

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.