Faculty The Faculty of HOLS Hospitality School faces challenges in manual administration, such as accumulating registration data, difficulty in data retrieval, risk of data loss, administrative inaccuracies, and time-consuming registration and attendance processes, increasing the risk of administrative errors. To address these issues, researchers designed a web-based information system using the waterfall model and PHP programming language. This system allows students to mark attendance using a QR code within a 5-meter radius, mentors to reschedule classes quickly and easily, and administrators to control administrative data accurately and efficiently. The result of this research is an information system product that facilitates academic administration management at HOLS Hospitality School, supports e-learning facilities, and simplifies coordination between students, mentors, and administrators.

Skin cancer has high incidence and fatality rates, making accurate and rapid detection crucial. This study developed a web-based skin cancer detection system using YOLOv8. The model detects seven types of skin cancer using a dataset of 17.366 annotated images. Methods included data collection, pre-processing, augmentation, model training, and performance evaluation using precision, recall, and mean Average Precision (mAP). Results show that the YOLOv8 model achieved a precision of 0.975 and a recall of 0.969. Evaluation with a confusion matrix demonstrated strong detection capabilities. A web interface was developed to allow users to upload images and view detection results in real-time. The YOLOv8-based skin cancer detection system provides accurate results and can be used as a tool for early diagnosis.