Recognitions of Bahasa Isyarat Indonesia (BISINDO) Alphabets Using SVM and Mediapipe

Bahasa Isyarat Indonesia (BISINDO), Indonesia's national sign language, serves as a primary mode of communication for a large portion of the population. This study explores a novel approach to BISINDO sign language recognition by leveraging Support Vector Machine (SVM) in conjunction with MediaPipe, a framework for real-time machine learning. MediaPipe has demonstrated strong performance on other datasets, and in this application, it extracts features from hand gesture images that are then classified by the SVM algorithm. Our model achieved an impressive average accuracy score of 98.82%, with an average precision of 97.34%, recall of 99.00%, and F1-score of 97.76%. In comparison to previous studies using HMM (which achieved an accuracy of 76.25%) and CNN (with an accuracy of 75.83%), our model demonstrates a significant improvement in recognizing BISINDO. Our model offers several distinct advantages. MediaPipe's robust feature extraction capabilities are particularly well-suited to capturing complex hand movements, allowing SVM to focus on high-quality input features that enhance classification accuracy. Unlike deep learning models such as CNN, which require substantial computational resources, our model remains lightweight and efficient, making it ideal for deployment on mobile and low-resource devices. This efficiency ensures that BISINDO recognition can be achieved in real-time and in practical applications, enabling a more accessible and reliable communication tool for the BISINDO-used community.