| Abstrakt: |
Quality of learning in the classroom is influenced by many factors. One of them is the academic emotions of the students. The emotion detection in the classroom cannot be done by using sensors attached to the body of the students, because it would disturb the concentration of the students. The proposed solution is by using unobtrusive emotion detection, e.g. by placing video capture equipment, which is not visible at the front of the student's desk. In this study, an RGB - Depth Microsoft Kinect camera is used to record facial expressions by considering the convenience factor of the students, speed of response time, and cost efficiency. A combination of Cohn-Kanade dataset and EURECOM dataset is used as the training set in machine learning with Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithm, with 8 sample of Asian race students (4 male and 4 female students). |
