<![CDATA[Since the prolonged COVID-19 pandemic, most human activities are seen with the concept of virtual meetings. This concept is helped by the use of an online conferencing platform. As a result, needs arise among the new society. The learning model of hybrid learning, or blended learning is a combination of face-to-face learning with e-learning. This learning method reduces teaching performance due to limited range of motion. Thus, hand tracking gesture recognition can be used as a solution to overcome this problem. This study aims to model a gesture recognition system with statistical and dynamic recognition. The method used in this research is CNN-based RT3D_16F which is used as dynamic motion prediction and Mediapipe hand pipeline which is used as static motion prediction. The data set used consists of 27 movement labels (includes 2 movement labels that shouldn't be recognized as specific moves).]]>
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