Abstract
Stand Up Comedy is one of the growing entertainment industries in Indonesia. The quality of the joke of the stand up comedian can be seen from the audience's response in the form of laughter and facial expressions. But it is very difficult to determine the audience's laughter level manually. Therefore, a tool is needed to measure the audience's laughter level more objectively. In this study, a machine learning model was designed and developed using a convolutional neural network to analyze the facial expressions of the stand-up comedian audience. The research stages include dataset collection, data preprocessing, model design, model training and model testing. The model was tested using artificial data from crowd emotion and live data from video crowd viewers. The results of this study show that the model can classify facial expressions in image and video data well. The model has an overall validation accuracy value of 72.42% with precision, recall, and F1-score values of 0.7341, 0.7309, and 0.7294, respectively, and a testing accuracy of 83.28% with precision, recall, and F1-score values of 0.8477, 0.8329, and 0.8297, respectively.
Published Version
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