Deteksi Kelelahan Wajah Mahasiswa Akibat Begadang Berbasis Convolutional Neural Network (CNN)

Abstrak

This research is entitled Detecting Physical Facial Changes Due to Staying Up Late in College Students Using Convolutional Neural Network (CNN). Staying up late or lack of sleep is a common habit among students due to academic pressure, social activities, and prolonged use of digital devices. This condition can cause changes in facial physical characteristics such as red eyes, dark circles under the eyes, and a dull appearance. Identification of these changes is generally still done subjectively, so a system is needed that can detect them automatically by utilizing image processing technology and artificial intelligence. This research aims to design and implement a Convolutional Neural Network (CNN) model to detect changes in facial physical characteristics due to staying up late in students. The dataset used is 330 facial images obtained by taking photos using a cellphone camera in JPEG format. The dataset consists of four categories: normal faces, dull faces, red eyes, and dark circles under the eyes. The data then goes through a pre-processing stage, dividing the dataset into 300 training data and 30 test data, and the model training process using the MobileNetV2 architecture with the Python programming language. Test results show that the CNN model is capable of classifying changes in facial physical characteristics with good performance. Based on an evaluation using a Confusion Matrix, the model achieved a precision of 89%, a recall of 87%, an F1-score of 86%, and an accuracy of 87%. The CNN method is considered effective in automatically detecting changes in facial physical characteristics due to staying up late in students.

Identifier

• DOI: https://doi.org/10.47065/bulletinds.v5i3.9871
• OpenAlex: https://openalex.org/W7166700330