<![CDATA[Effective communication is a fundamental human need; however, for people with hearing impairments in Indonesia, interaction relies heavily on the Indonesian Sign Language System (Sistem Isyarat Bahasa Indonesia – SIBI). Although deep learning has been widely applied in sign language recognition, comprehensive comparative studies focusing specifically on SIBI remain limited, particularly in evaluating the performance gap between different neural network architectures. This study addresses this gap by comparing the effectiveness of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in classifying SIBI hand gesture images. An augmented SIBI dataset was trained using the Adam optimizer to improve generalization and recognition performance. The experimental results reveal a significant performance difference between the two models, where CNN achieved a precision, recall, and F1-score of 94%, while RNN obtained a precision of 76% recall of 74%, and F1-score of 73%. These findings demonstrate that CNN is substantially more effective for image-based SIBI recognition because it extracts spatial features more effectively than the sequential processing mechanism of RNN. This research contributes empirical evidence for selecting appropriate deep learning architectures in SIBI recognition systems and offers practical implications for developing more accurate and reliable assistive communication technologies in educational and accessibility contexts.]]>
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