<![CDATA[Sign language is a vital communication medium for individuals with hearing and speech impairments. In Indonesia, more than 2.6 million people experience hearing disabilities, most of whom rely on Bahasa Isyarat Indonesia BISINDO for daily interaction. However, limited public understanding and the scarcity of professional interpreters continue to hinder inclusive communication. Recent advancements in computer vision and deep learning have enabled camera-based sign language recognition systems that are more affordable and practical compared to sensor-glove solutions. this study presents a comparative analysis between EfficientNetV2-B0 and MobileNetV3-Large in recognizing BISINDO hand sign alphabets using MediaPipe landmarks. The dataset was derived from BISINDO video recordings, from which hand landmarks were extracted using MediaPipe Hands and subsequently converted into two-dimensional skeletal images. In total, 10,309 skeletal images representing BISINDO alphabets A–Z were generated and used for model training and evaluation. Both models were trained under identical configurations using TensorFlow. The results show that MobileNetV3-Large achieved 89.67% test accuracy and an F1-score of 89.76%, while EfficientNetV2-B0 obtains 95.98% test accuracy and an F1-score of 95.93%. These findings highlight the trade-off between the higher classification accuracy of EfficientNetV2-B0 and the superior computational efficiency of MobileNetV3-Large. This research contributes to the development of lightweight, high-performance BISINDO recognition systems for assistive communication applications.]]>
