Body Temperature Classification System Based on Fuzzy Logic and The Internet of Things

Abstract

Health technology has become more efficient thanks to telecommunications and information technology. One of the utilizations is IoT detecting a person's body temperature condition. This research aims to produce a tool that can classify human body temperature by implementing fuzzy logic methods and the Internet of Things so that early prevention can be carried out against hyperthermia and hypothermia sufferers and can be used as advice for further examination to doctors. The phases in this study are analysis requirement, system design, implementation, and testing. The tools incorporated in this system are intended to measure body temperature and can classify the measured body temperature into specific categories. This research utilizes the Sensor Proximity E18-D80nK to detect the presence of an object concerning the sensor. Additionally, the system employs MLX90614 sensors for temperature measurement. The results of this research are that the use of the DHT22 sensor for measuring room temperature and the MLX80614 sensor for non-contact body temperature respectively produces a high level of accuracy, namely 98.22% and 98.29%. In addition, the implementation of the fuzzy logic algorithm succeeded in achieving 100% accuracy in classifying body temperature data, showing the effectiveness of this method in detecting human body temperature.