<![CDATA[Excessive classroom noise poses a serious challenge to effective learning, particularly in physics education, where conceptual clarity and experimental observation are essential. Addressing this issue, this study aims to design and evaluate the STEMDUNOISE prototype an Internet of Things (IoT)-based device that monitors and classifies real-time classroom noise levels to support better learning environments. The research employed a Research and Development (RD) method based on the Borg and Gall model, encompassing needs analysis, system design, prototyping, and preliminary trials. The system integrates an ESP32 microcontroller, MAX9814 sound sensor, ESP32-CAM for automatic video capture, and visual alerts through OLED and LED displays. Testing was conducted in both laboratory and classroom settings to ensure the accuracy of performance and user acceptability. Results show that STEMDUNOISE effectively categorizes noise into three levels: low (45–55 dB), medium (55–65 dB), and high (65–90 dB), providing immediate feedback through colored LEDs and real-time data via the Blynk platform. Its novelty lies in the combination of real-time sound analysis and automated video documentation in a compact and reusable tool. Conclusions indicate that the system enhances noise awareness, supports classroom management, and improves student engagement and focus during physics instruction. The findings contribute significantly to physics education by fostering distraction-free environments, promoting self-regulated learning, and offering data-driven solutions for educational policy and instructional improvement.]]>
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