Teaching the Machine, Narrating the Self: Teachers’ Lived Experiences in Implementing IoT-Based Transformative Learning
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September 1, 2025
April 29, 2026
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Gas Leak, MQ-2 Sensor, Transformative Learning
Abstract
Background. Gas leaks in households pose severe risks of explosion and poisoning. Conventional detection methods often lack the responsiveness required for early prevention, necessitating more modern, IoT-based monitoring systems.
Purpose. This study aims to develop and evaluate an IoT-based learning model through a gas leak detection system integrated with Telegram API to enhance both environmental safety and student digital literacy.
Method. This experimental research was conducted over a sixteen-week period. The system was designed using an MQ-2 gas sensor and a NodeMCU ESP8266 microcontroller. The methodology involved four stages: system design, device implementation, performance testing across varying gas concentrations (120–800 ppm), and pedagogical evaluation of the learning model's impact on students' technical competencies.
Results. Technical testing demonstrated that the system is highly responsive, with an average notification delay of 2 to 5 seconds depending on gas density. At dangerous levels (600–800 ppm), the system consistently achieved a rapid 2-second response time with a 100% notification success rate via Telegram.
Conclusion. The study’s novelty lies in its dual-purpose framework, which functions not only as a high-precision safety tool but also as a structured pedagogical medium that bridges the gap between theoretical IoT concepts and practical environmental responsibility.
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Authors
Copyright (c) 2026 Arfanda Anugrah siregar, Tongam E Panggabean, Erwinsyah Simanungkalit, Muhammad Hizbullah Rais Siregar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
