Smart Biogas Control for Communities Using Gaussian Naïve Bayes - Koesmarjanto; Ria Amanda Salsabella; Junus, Mochammad (Faculty of Engineering, Universitas Brawijaya, 2025)
The design and implementation of an intelligent biogas quality monitoring and control system that combines machine learning, actuator automation, and Internet of Things (IoT) technology is presented in this research. The system uses a thermocouple type K, MPX5700, MQ-4, and MQ-135, among other environmental sensors, to measure temperature, pressure, CO?, and CH? in real time. An ESP32 microcontroller processes sensor data using the Gaussian Naïve Bayes algorithm to categorize biogas quality into three classification, namely Good, Moderate, and Poor. A servo motor is utilized to control a valve that either permits or prohibits the flow of biogas to a generator based on the classification output. Through the Blynk IoT platform, the system has the capacity to be remotely monitored. Results from experiments with 40 biogas data demonstrated that the system had good precision and recall in each category and an overall accuracy of 92.5%. The approach exhibits dependability, affordability, and suitability for community-based biogas management in rural and semi-urban evironments.