<![CDATA[The Indonesian Sign Language System (SIBI) is a translator of sign language into text or speech. This research aims to bridge communication between ordinary people and speech impaired people through the introduction of SIBI sign language using the YOLO algorithm. This research uses 24 alphabets which are divided into 4 groups, where each alphabet has 20 image data which is divided into 70% train data, 25% valid data, and 5% test data. The train data was then added with augmented data from Roboflow which was then carried out using a training process using a batch number of 16 and epochs of 100. The results of the research show that the YOLO algorithm can detect SIBI sign language alphabet gestures using confusion matrix testing and achieve quite good performance, as shown by the results F1 Score: Group 1 was 90.90%, Group 2 was 97.1%, Group 3 was 90.90%, and Group 4 was 83.8%. Other factors such as hand size, lighting conditions, and variations in data position also affect detection accuracy. A limitation in this research is that the alphabets J and Z were not included because these two alphabets not only use shape patterns, but also gesture patterns.]]>
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