PREDIKSI KUALITAS UDARA BERDASARKAN PARAMETER CO DAN CO2 MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

Abstract

This study aims to predict carbon monoxide (CO) and carbon dioxide (CO2) levels as indicators of air quality using the Artificial Neural Network (ANN) method with a 21-20-7 architecture. Data was collected over 28 days using the AQMSense device, with the first 21 days used for training and the remaining 7 days for testing. The results show that the ANN model effectively recognizes historical data patterns, indicated by a regression value close to 1. The prediction accuracy reached 89.96% for CO and 95.84% for CO2, demonstrating strong model performance. ANN proves to be an effective tool for daily air quality prediction and holds potential as a decision-support system for air pollution mitigation