Olivia Kembuan, Haryanto, Mochamad Bruri Triyono
This study focuses on developing an automatic Indonesian sign language (SIBI) recognition system using a convolutional neural network (CNN). Sign language is essential for communication among deaf and hard-of-hearing individuals, and automatic recognition helps improve accessibility and inclusivity. CNNs are chosen for their ability to learn image features automatically, eliminating manual extraction and improving classification accuracy. The SIBI dataset used contains 5,280 images of 26 letters, divided into training and validation sets. In early training, the model achieved low accuracy (3.63% training, 3.33% validation), but after five epochs, it significantly improved to 97.58% for training and 100% for validation. © 2025, Institute of Advanced Engineering and Science. All rights reserved.
Doctoral Program in Technology and Vocational Education, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Informatics Engineering Study Program, Faculty of Engineering, University of Manado, Manado, Indonesia; Educational Research and Evaluation, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
