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All Journal EVOLUSI : Jurnal Sains dan Manajemen
Muhammad Hisyam Syafaat
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Computer Science & IT

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Optimasi Hyperparameter CNN Berbasis MediaPipe untuk Peningkatan Akurasi Pengenalan SIBI Denny Pribadi; Apip Supiandi; Taufik Hidayatulloh; Rinda Restiawati; Muhammad Hisyam Syafaat; Muhammad Rendi Rizaldi
Evolusi : Jurnal Sains dan Manajemen Vol. 14 No. 2 (2026): Periode September 2026
Publisher : LPPM Universitas Bina Sarana Informatika Kampus Kabupaten Banyumas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v14i2.12812

Abstract

Sistem Bahasa Isyarat Indonesia (SIBI) is a visual communication medium used by individuals with hearing impairments; however, public awareness and understanding of sign language remain limited, creating significant communication barriers in daily life. This study aims to develop a SIBI alphabet recognition system based on deep learning by utilizing a Convolutional Neural Network (CNN) approach supported by computer vision using MediaPipe. The research stages include hand image acquisition, preprocessing through normalization and augmentation, and model training using a CNN architecture. The dataset is divided into 80% for training, 10% for validation, and 10% for testing to evaluate the model’s generalization performance. Experimental results show that the model achieves a training accuracy of 99.72% and a validation accuracy of 96%, indicating highly effective performance in real-time SIBI gesture classification. Therefore, this study contributes to the development of assistive technologies that facilitate inclusive communication for individuals with hearing impairments in Indonesia.
Co-Authors Apip Supiandi Muhammad Rendi Rizaldi Pribadi, Denny Rinda Restiawati Taufik Hidayatulloh