Implementation of a convolutional neural network method to predict university students’ emotional extent in online learning - ., Baharuddin; Gultom, S; Restuati, M; Mansyur, A; Fibriasari, H; I Pane, I; Pujia, S; S G Galamgam, V; P Hacla, R; A Rais, M; Effendi, R; R Pane, M (International Journal of Engineering and Technology, 2022)
A learning system that was directly face-to-face in class, had to be substituted with a learning system that is virtually integrated with the inter-net (online learning). The system uses online learning that connects teaching staff with the learning participants. Tiredness and laziness start-ed to be felt among the participants. This is worried to affect nonoptimal learning outcomes. During lessons, teaching staff often focuses on learning materials without knowing the emotions of their learning participants. Indirect online learning becomes a new challenge in adaptation; teaching staff and learning participants are required to adapt to existing conditions. With the problems above, the writer has an idea in devel-oping a prototype system that is developed to be able to help teachers in evaluating manners and emotions of students in online learning. Facial detection is a technology for detecting facial features such as the nose, eyes, and mouth. Development of facial detection that has un-dergone upgrades until now can differentiate if something is a facial feature or not and detect more than one face. This research aims to help teaching staff in evaluating learning participantsâ€™ affective behavior online by applying a Convolutional Neural Network method that has been trained to be able to predict emotional extents through facial expression.Â Â