PENGENALAN AKTIVITAS MANUSIA DENGAN METODE RESNET50 DAN VGG16
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Abstract
The field of Artificial Intelligence (AI) is rapidly advancing in the modern era. Various applications such as human activity recognition, product recognition through images, voice detection, and more have gained significant attention. Human activity recognition, in particular, is a trending area of focus, especially in the domain of Intelligence Systems. The goal of human activity recognition can be implemented for diverse purposes, including child monitoring, crowd density estimation in a room, and surveillance cameras. This research focuses on human activity recognition using Convolutional Neural Networks (CNN), employing the ResNet50 algorithm and comparing it with VGG16. The studied activities are categorized into 15 classes, such as sitting, using a laptop, sleeping, dancing, running, eating, and others. The dataset is divided into training and validation data. Subsequently, the ResNet50 and VGG16 algorithms are employed to train models, and the accuracy of the trained models is evaluated. The research results indicate that the model trained with the ResNet50 algorithm performs slightly better than VGG16, achieving an accuracy of 95% compared to VGG16's 94%.
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