<![CDATA[This study aims to address the limited auditory range of conventional alarms at the Gunungsari 150 kV GIS by developing an Internet of Things (IoT)-based early warning system. Using a descriptive-analytical experimental study approach, this research conducted systematic observations and functional testing of a prototype that integrates an ESP32 microcontroller and a KY-037 sound sensor. Key findings indicate that setting the threshold at 60 effectively distinguishes alarm signals from background noise, with notification transmission to the Telegram app taking less than 5 seconds. The system architecture, which separates the logic for local visual indicators (LEDs) from network notifications, ensures the reliability of alerts even in the event of internet connectivity issues. These findings contribute to strengthening the “human-in-the-loop” monitoring model, which enhances the effectiveness of remote monitoring by substation personnel. The practical implication is the creation of a more responsive and accountable energy infrastructure protection system. Further research is recommended to integrate network redundancy modules and autonomous power sources to anticipate the risk of total power outages.]]>
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