PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK ANALISIS PRODUKSI BIOGAS DALAM PENGOLAHAN SAMPAH ORGANIK BERBASIS IOT

Application of Convolutional Neural Network (CNN) Method for Biogas Production Analysis in IoT Based Organic Waste Treatment

Abstract

 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.