Identifying the best models for BISINDO alphabet gesture classification to support the communication needs of the deaf community

The object of this research is the BISINDO alphabet gestures which are static hand movements used by the Deaf community in Indonesia to communicate, with each letter having a unique hand pattern influenced by culture and regional variations. The problem solved is the accuracy and performance of BISINDO hand gesture classification which is less than optimal due to complex gesture variations and computational limitations on lightweight devices. This study examines the performance of BISINDO alphabet gesture classification using deep learning models with 4 architectures: VGG-19, ResNet-50, MobileNetV2, and Inception-V3. This object is a collection of images used as a dataset consisting of 10,400 images (7,280 training, 2,080 validation, and 1,040 testing). The results show that the MobileNetV2 and VGG-19 architectures achieve the highest accuracy of 100%, followed by Inception-V3 (99%) and ResNet-50 (98%). The results of this study indicate that the superior performance of MobileNetV2 is due to its efficient depthwise separable convolutional architecture, while the superiority of VGG-19 lies in its very deep architecture and the use of small convolutional filters to capture very detailed hierarchical features of gestures. Meanwhile, Inception-V3 excels thanks to the inception module that captures gesture features at various scales. The results of this performance comparison, MobileNetV2 is the most superior because of its computational efficiency that supports high performance, as well as adaptation to complex variations in BISINDO alphabet gestures. The results of this study can also be applied in the fields of education and communication for the deaf community, especially in embedded applications, thus enabling real-time sign language accessibility