Klasifikasi Gerakan Yoga dengan Model Convolutional Neural Network Menggunakan Framework Streamlit - Mohammad Fikri Nur Syahbani; Nur Ghaniaviyanto Ramadhan (Universitas Budi Darma, 2023)
Indonesian people are not fit and lack sports activities, therefore one of the alternative sports activities is yoga. Yoga is a type of exercise that has two important components, namely breathing and movement. Yoga movements also vary and can be distinguished from body curves, but ordinary people may not be familiar with yoga movements. With advances in technology and computer performance intelligence, it is now possible for computers to recognize an image for object recognition, namely detecting yoga movements using the digital image classification method. To make it easier to classify yoga movements, you can use the CNN model. Convolutional Neural Networks (CNN) are a combination of artificial neural networks with deep learning methods. The CNN process will carry out a training and testing process for yoga movements so that an image classification can be determined from the type of yoga movement. The image of the yoga movement is divided into 80% for training and 20% for testing. The training process is carried out using two different scenarios by differentiating the input image size, batch size, optimizer. The dataset consists of goddess, plank, tree, warrior2, downdog movements. The highest accuracy results are 94.10% using 170 x 170 image input, batch size 32, RMSprop optimizer. The results of testing a total of 40 images of yoga movements, 37 images were correctly guessed. The model that has been trained is implemented into the website using the Streamlit framework.