<![CDATA[This study aims to develop a facial expression classification system to identify indications of anxiety using the Convolutional Neural Network (CNN) method, utilizing the FER2013 dataset, which consists of seven emotion categories: angry, disgust, fear, happy, sad, surprise, and neutral. The research stages include data preprocessing, CNN model training, model performance evaluation, and real-time testing using a webcam. The developed system is capable of recognizing facial expressions and classifying them into PANIC, NORMAL, and UNCERTAIN statuses based on the confidence scores generated by the model. The expressions of fear, sad, angry, and disgust are interpreted as indicators of anxiety, whereas happy, neutral, and surprise are interpreted as normal conditions. Based on the testing results using the FER2013 test data, the CNN model achieved an accuracy of 92.58%, a precision of 93.48%, a recall of 92.58%, and an F1-score of 92.27%. The results demonstrate that the CNN method can be effectively utilized for facial expression classification and to provide early indications of anxiety based on the detected expressions. However, the system's performance remains influenced by factors such as lighting, camera quality, facial positioning, and limitations in the quantity and distribution of data within the utilized dataset. Therefore, further development is required to enhance the accuracy and reliability of the system in detecting indications of anxiety in real-time.]]>
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