<![CDATA[This study discusses the development of an Internet of Things (IoT)-based myoelectric signal monitoring system using Electromyography (EMG) as an interactive learning medium in biomedical instrumentation education. The proposed system utilizes an EMG sensor to capture muscle activity signals, while an ESP8266 microcontroller transmits the acquired data in real time to the Blynk platform for visualization and monitoring purposes. Experimental results demonstrate that the system operates properly and exhibits a strong linear response between load variation and EMG signal voltage. Validation conducted by media experts and testing involving university students indicate that the developed learning media is highly feasible for educational use, achieving an average feasibility score above 90% and a usability score of 71.5 out of 75. Despite these positive results, several improvements are recommended for future development. These include enhancing electrode quality and adding shielding to the sensor cables to minimize electromagnetic interference. Furthermore, future studies may integrate data logging features for long-term analysis and implement machine learning algorithms for automatic muscle activity pattern classification. The integration of these features is expected to improve the functionality and applicability of the proposed system in engineering and biomedical education.]]>
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