Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network

Mambang, Finki Dona Marleny (2023) Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network. [Karya Dosen UNISM]

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Abstract

Learning content can be identiêed through text, images, and videos. This study aims to predict the learning content contained on YouTube. The images used are images contained in the learning content of the exact sciences, such as mathematics, and social science êelds, such as culture. Prediction of images on learning content is done by creating a model on CNN. The collection of datasets carried out on learning content is found on YouTube. The êrst assessment was performed with an RMSProp optimizer with a learning rate of 0.001, which is used for all optimizers. Several other optimizers were used in this experiment, such as Adam, Nadam, SGD, Adamax, Adadelta, Adagrad, and Ftrl. The CNN model used in the dataset training process tested the image with multiple optimizers and obtained high accuracy results on RMSprop, Adam, and Adamax. There are still many shortcomings in the experiments we conducted in this study, such as not using the momentum component. The momentum component is carried out to improve the speed and quality of neural networks. We can develop a CNN model using the momentum component to obtain good training results and accuracy in later studies. All optimizers contained in Keras and TensorFlow can be used as a comparison. This study concluded that images of learning content on YouTube could be modeled and classiêed. Further research can add image variables and a momentum component in the testing of CNN models.

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