Skripsi
IMPLEMENTASI PENILAIAN KONDISI PASIEN BERBASIS IoMT DENGAN LOGIKA FUZZY SUGENO
Real-time health condition monitoring is an important requirement in supporting more effective and efficient healthcare services. The development of the Internet of Medical Things (IoMT) enables medical devices to be connected to the internet, allowing health data to be monitored remotely. This study aims to design and implement an IoMT-based patient health monitoring system using the MAX30102 sensor and the Sugeno Fuzzy Logic method to classify patient health conditions based on heart rate (BPM), blood oxygen saturation (SpO₂), and age. The developed system utilizes the MAX30102 sensor to measure BPM and SpO₂ values, an ESP32 microcontroller for data processing and internet connectivity, and the Blynk application as a real-time monitoring platform. The Sugeno Fuzzy method is employed to process input data and classify patient conditions into three categories: Healthy, Less Healthy, and Critical, based on a rule base developed according to medical references and user age groups. The results show that the system successfully performs real-time monitoring and classification of patient health conditions. Based on the conducted tests, data from a 22-year-old subject with a BPM of 87.40 and SpO₂ of 100 were classified as Healthy, data with a BPM of 99.90 and SpO₂ of 100 were classified as Less Healthy, and data with a BPM of 122.30 and SpO₂ of 85 were classified as Critical. Furthermore, the manual calculation results were consistent with the results generated by the system for all test data, indicating that the implementation of the Sugeno Fuzzy method was successfully carried out according to the designed system.
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