Abstract:
In this challenging digital era, stress has become an inseparable part of daily life, affecting all ages. Although researchers
have discussed stress detection extensively, there are few practical and accessible applications for users. This research aims to
develop a mobile application utilizing a modified Convolutional Neural Network (CNN) for stress detection based on facial
expression, thereby enabling more effective and efficient stress detection and management. The well-known CNN architectures, i.e.,
DenseNet201, MobileNetv2, and ResNet50, could have been more optimal for detecting stress from facial expressions. Hence, the
CNN architectures are modified to enhance the accuracy of the task by adding dropout layers, Pooling2D, and Relu Activation. The
research was conducted through data collection, image pre-processing, training the model with the modified CNN architectures, and
developing a mobile application for stress detection. With the modifications made, this research succeeded in increasing the model's
accuracy in detecting stress from facial expressions, where the modified DenseNet201 achieved the highest accuracy, from 75.90%
to 77.83%. The mobile application can detect stress based on facial expression image obtained from file or camera. In conclusion,
using artificial intelligence technology, especially through modifying the CNN architecture, enhances the accuracy of stress detection
from facial expressions, and the developed mobile application offers a practical solution.
