<![CDATA[This research aims to design and implement a web-based teacher attendance system that utilizes facial recognition technology through the YOLOv8 algorithm as a solution to the conventional paper-based attendance system, which is prone to recording errors, data manipulation, and potential information loss. Data collection was conducted by recording short videos of 24 teachers and staff at SD Negeri 1 Purbalingga Lor under various lighting conditions and viewpoints, which were then converted into an image dataset using the Roboflow platform. The dataset was processed through several preprocessing stages including video-to-image conversion, image resizing, augmentation, and data splitting for training, validation, and testing purposes. The YOLOv8s model was chosen due to its ability to detect faces in real time with high accuracy, as demonstrated by training results showing an mAP of 98.6%, Precision of 97.8%, and Recall of 98.5%. The integration of the model into a backend Flask-based application enables the attendance process to be carried out automatically and in real time, while functional testing using the Black Box Testing method confirms that the face detection feature operates as designed, achieving an accuracy of 93% under optimal lighting conditions. Consequently, this research successfully presents an innovative digital solution that not only enhances the efficiency of attendance administration but also minimizes the risks of data manipulation and recording errors in educational environments.]]>
