Literature Review : Implementasi Machine Learning Dalam Pengenalan Bahasa Isyarat Indonesia (BISINDO)
Keywords:
BISINDO, Machine Learning, Deteksi Bahasa Isyarat, Inklusi KomunikasiAbstract
Recognition of Indonesian Sign Language (BISINDO) is one of the main challenges in supporting communication for deaf and mute people. This study aims to examine the application of machine learning algorithms in BISINDO recognition through a systematic review of several scientific journals. The analysis was conducted to identify trends, research gaps, and the potential contribution of this technology in supporting inclusive communication. The results show that algorithms such as CNN, YOLOv8, and Mediapipe provide high accuracy in hand gesture detection and classification. Further research is needed to refine this technology, especially in terms of representative datasets and real-time implementation.
References
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Wibowo, R. K. A., Sanjaya, A., & Mahdiyah, U. (2024). Implementasi YOLOv8 pada Pengenalan Sistem Isyarat Bahasa Indonesia. Prosiding SEMNAS INOTEK, 8, 139–146. https://proceeding.unpkediri.ac.id/index.php/inotek/
Saputra, D., & Hadiwandra, T. Y. (2024). Classification Of Letters And Numbers In BISINDO Using The Convolutional Neural Network Method. IJIRSE: Indonesian Journal of Informatic Research and Software Engineering, 4(2), 88–95. https://journal.irpi.or.id/index.php/ijirse
