<![CDATA[The development of biogas technology in Indonesia faces challenges in production monitoring and prediction accuracy due to the non-linear complexity of data. This study aims to implement a Convolutional Neural Network (CNN) method integrated with Internet of Things (IoT) technology to analyze and predict biogas production parameters. The system utilizes an ESP32 microcontroller with MQ-4, DHT11, and pH sensors for real-time data acquisition sent to a cloud service. The prediction model was developed using a 1D-CNN architecture trained on 2,400 sensor data points collected at 30-second intervals. Results demonstrate that the CNN model is highly effective in predicting methane gas concentration, achieving an R2 of 0.99661 and a MAPE of 1.3888%. Temperature and pH predictions yielded R2 values of 0.8982 and 0.6633, respectively, with MAPE remaining below the 10% threshold. Overall, the integration of IoT and CNN provides accurate and stable predictions, serving as an intelligent reference for optimizing biogas production from household organic waste.]]>
Copyrights © 2026
