<![CDATA[This study is motivated by the limitations of the conventional Body Mass Index (BMI) method, which applies rigid classification thresholds and is therefore unable to flexibly represent body conditions, particularly for values near category boundaries. This research aims to develop a body status classification system based on BMI using a fuzzy logic approach integrated with a real-time digital monitoring system. The system is designed using an ESP32 microcontroller, utilizing a load cell sensor for weight measurement, an ultrasonic sensor for height measurement, and RFID for user identification. The measured data are processed into BMI values and classified using a fuzzy logic method with a maximum membership approach through triangular and trapezoidal membership functions. Furthermore, the data are transmitted to a cloud-based database and displayed via a web interface in the form of tables and graphs to support monitoring activities. The testing results indicate that the system achieves good accuracy, with an average measurement error of 0.94 cm for height and 0.75 kg for weight. In addition, the fuzzy method provides more adaptive classification results compared to the conventional approach, particularly for BMI values in transition regions between categories. The developed monitoring system is also capable of displaying data in real-time with a high transmission success rate. Therefore, this system not only functions as a measurement tool but also serves as an educational medium for understanding fuzzy logic concepts and the implementation of Internet of Things-based systems.]]>
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