<![CDATA[Communication is a fundamental human necessity; however, for the deaf community, barriers to interaction with the general public remain a significant challenge due to limited literacy in sign language. This research aims to implement a hand gesture recognition system capable of translating the alphabet of the Indonesian Sign Language System (SIBI) in real-time. The MobileNetV2 architecture was selected as the base model due to its efficiency in processing data on resource-constrained devices without significantly compromising accuracy. The methodology involves several crucial stages, beginning with image pre-processing—including resizing and image normalization—to the application of data augmentation strategies such as rotation, shifting, and brightness adjustment to enhance the model's generalization capabilities in real-world conditions. The dataset comprises SIBI alphabet classifications from A to Z, collected with high variability to minimize the risk of overfitting. The results demonstrate that the use of depthwise separable convolutions in MobileNetV2 allows the system to perform gesture detection with high responsiveness and low computational overhead. Through hyperparameter optimization, this model is expected to achieve optimal accuracy, providing a practical and inclusive communication tool for the deaf community within social environments and public services.]]>
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