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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Conference on Innovation and Application of Science and Technology (CIASTECH) Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian
Sutan Faisal
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Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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OBJECT DETECTION OF INDONESIAN SIGN LANGUAGE SYSTEM USING YOLOV7 METHOD Genta Kusuma Atmaja; Hikmayanti, Hanny; Rahmat, Rahmat; Sutan Faisal
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2468

Abstract

SIBI or Indonesian Sign Language System, a communication language for the deaf community in Indonesia. SIBI has the advantage of conveying information between individuals. SIBI integrates various hand signals to replace words in Indonesian, enabling effective and inclusive communication. SIBI still lacks educational programs in the community and identifying SIBI has become a major problem in facilitating communication for normal people with hearing impairments. The proposed solution in the development of SIBI detection is to utilize artificial intelligence (AI) technology and digital image processing. This program focuses on understanding the typical hand movements used in SIBI. So a program was created to detect hand language using the YOLOv7 architecture. This study aims to educate those who are not yet familiar with the SIBI hand object that will be detected., especially in the context of sign language recognition for singular pronouns. The research method used is data acquisition by collecting a dataset of 320 images, data annotation by labeling objects on the hand, image pre-processing with augmentation, resizing, and cropping, model training with 100 epochs on both pre-trained models (yolov7 and yolov7-x), and testing is done by detecting 20 images from each class category totaling 5. The dataset used for training 300 images and validation 20 images. The results of the yolov7 model accuracy value are mAP @ .5 of 99.5% and mAP @ .5: .95: of 90.5%. The accuracy of the yolov7-x model is mAP @ .5 99.6% and mAP @ .5: .95: of 75.8%. And the results of the test carried out with 20 images, out of 20 correct images only 18 and the accuracy value obtained is 90%.
Co-Authors Adi Rizky Pratama Afga Ilyasa Ahmad Fauzi Alifa, Naila Ratu Amril Mutoi Aqib Zhaky Ayuni Evita Rahayu Azzahra, Wava Lativa Bagus Priambada Baihaqi, Kiki Ahmad Daffa Alwan Deni Yusup Genta Kusuma Atmaja Habib Abdullah Hanny Handayani Hanny Hikmayanti Handayani Ilham Azis Annaba Indra, Jamaludin Isnaeni Hidayah Jorke Alkawiyu Lestari, Santi Arum Puspita Madi Naashir Muhammad Chayim Mustabinnur Nurofik Rahmat Rahmat Rija Nur Hijriyya Rissa Ilmia Agustin Rizki Nur Annisa Rizki Septian Akbar Roby Rupiat Santi Lestari Santi Pertiwi Hari Sandi Siregar, Amril Mutoi Suja Priyanto Syamsul Tri Andika Tatang Rohana Tohirin Al Mudzakir Windyani Eka Putri Yana Cahyana