<![CDATA[Indoor air quality is an important factor for human respiration. In enclosed spaces, especially with increased activity and the number of occupants, pollutants such as carbon dioxide may increase, while oxygen levels may decrease. Therefore, a classification system is needed to determine indoor air quality. In this study, we designed an air quality measurement device using two types of gas sensors, namely carbon dioxide and oxygen sensors. The carbon dioxide gas sensor used was the MG-811, while the oxygen gas sensor used was the Gravity I2C. In addition, an artificial neural network was implemented as the air quality classification method, divided into three categories: normal, wary, and dangerous. The classification process was divided into two stages: training and testing. Based on the experimental results, an error value of 0.016 was obtained with 274 epochs during the training process. Meanwhile, in the testing process, the achieved accuracy was above 90 percent, indicating that the artificial neural network was successfully implemented for air quality classification.]]>
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