<![CDATA[Loss of consciousness (fainting) is a critical condition that requires prompt treatment, especially in the context of elderly health services and independent patient care. This research aims to develop a web-based information system that is able to detect fainting events in real-time using the You Only Look Once (YOLO) algorithm version 11, which is one of the latest approaches in deep learning-based object detection. The system is designed to monitor video from the surveillance camera directly, make visual inferences of the patient's posture, and provide automatic notifications if a loss of consciousness condition is detected. The dataset was obtained from the Roboflow platform and consists of 9,081 annotated images representing the fainting position. The YOLOv11 model was trained and tested using training data sharing, validation, and testing methods. The test results showed that the model achieved mAP, precision, recall and F1-score values of 98.70%, 98.00%, 97.30% and 97.65%, respectively. The developed information system is able to display the detection visually through the bounding box on the dashboard and record the time of the incident. With this performance, this system shows great potential in improving patient safety through intelligent monitoring and automated response in hospital, nursing home, and residential environments. This research also opens up opportunities for the development of more adaptive AI-based health monitoring systems and computer vision in the future.]]>
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