<![CDATA[A patient should not be in a psychologically worrisome condition for fear of lowering the temperature causing slow healing to last a long time. Prolonged anxiety will develop into stress, so it is very necessary to detect anxiety early before anxiety persists and results in stress. However, these anxiety symptoms are related to the same psychological factors and contain uncertainties that cannot always be controlled and monitored by doctors. The purpose of this research is to create a system that can monitor the patient's anxiety symptoms in real-time based on the Internet of Things (IoT) so that doctors can determine the right treatment to maintain the patient's psychological condition. The system detects responses in humans when someone feels anxious, namely heart rate, body temperature, and sweat intensity which are detected using sensors. The information obtained from the sensor becomes input parameters which are then processed using fuzzy logic to detect early symptoms. Fuzzy logic was chosen because it is a method for solving fuzzy problems with the uncertainty of the threshold value of symptom change. The output is in the form of anxiety symptoms which are divided into 3 (three) symptoms, namely normal, mild, and severe. The research method used is the SDLC (System Development Life-Cycle) method. the results based on the black box test state that the entire functional system works according to its function, the white box test results state that all logic is correct and appropriate. The results showed that the system that had been built succeeded in monitoring anxiety in patients with a system accuracy of 98%.]]>
