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All Journal Media Journal of General Computer Science (MJGCS)
Arahmad taupiq
Department of Informatics, Dinamika Bangsa University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Identification of Indonesian Sign Language System Using Deep Learning in Yolo-based Arahmad taupiq; Muhammad Wildan Fajri; Dannylee
Media Journal of General Computer Science Vol. 1 No. 2 (2024): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v1i2.22

Abstract

Deafness, or hearing impairment, refers to the loss of auditory capability in one or both ears. Deaf communities often develop sign languages to facilitate communication. Sign language, which employs hand movements, is commonly adopted by individuals with hearing impairments. In Indonesia, two primary sign languages are used: BISINDO (Bahasa Isyarat Indonesia) and SIBI (Sistem Isyarat Bahasa Indonesia). The main distinction between these languages is that BISINDO employs both hands for signing, whereas SIBI uses only one hand. Individuals with hearing impairments face significant communication challenges. This study focuses on the detection of alphabets in the Indonesian Sign Language System (SIBI) using YOLO v5. The objective is to recognize alphabetic characters through hand gesture signals. Experimental results indicate a detection success rate of 95.38%, accurately identifying 23 out of the 24 tested letters.
Co-Authors Dannylee Muhammad Wildan Fajri