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All Journal Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Siti Abiola Fatika Abiola
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Pengenalan Kata Bahasa Isyarat Fingerspelling Menggunakan Metode Convolutional Neural Network Siti Abiola Fatika Abiola; Munawir munawir; Nurul Fadillah
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 3 No 2 (2022): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v3i2.7565

Abstract

Sign language is a way of visual communication used by people who have limitations in carrying out normal verbal communication. Alphabet sign language is a basic tool used by educators to teach the deaf and mute. However, many people have difficulty communicating with these circles due to a lack of public knowledge about hand sign language. Research on hand sign language has made great progress in processing static images but is still experiencing problems due to difficulties in processing dynamic images/videos considering that most of hand sign language is represented by body, hand and facial movements. This research uses the Convolutional Neural Network (CNN) method in real time. The research was conducted through the stages of acquisition. The results of this study were conducted in 3 experimental categories, namely trials of all sign language word recognition objects based on angles obtained an accuracy of 85% and trials of all sign language word recognition objects based on distance yielded an accuracy of 93.3% and sign language word recognition based on random positions received an accuracy of 85 %.
Co-Authors Munawir Munawir Nurul Fadillah