Design and Development of an IoT-Based Air Quality Monitoring System to Support Inspection Processes - Mohamad Agil Saputra Kantu; Serina Zera Patricia Tumoka; Sitti Nurhazriah Asraka; Maureen Langie; Harson Kapoh (International Journal Labs, 2026)
Air quality degradation caused by industrial activities, urbanization, and increasing transportation has become a significant environmental issue affecting human health and ecosystem sustainability. Conventional air quality inspection methods often require lengthy measurement processes and are unable to provide real-time information, resulting in limitations in continuous monitoring and decision-making. This research aims to design and develop an Internet of Things (IoT)-based air quality monitoring system integrated with the Mamdani Fuzzy Logic method to support real-time inspection processes and improve air quality classification accuracy. The research employed a Research and Development (R&D) approach using the ADDIE development model, which consists of the analysis, design, development, implementation, and evaluation stages. The developed system utilizes an ESP32 microcontroller integrated with multiple sensors to measure particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) parameters. Data transmission is conducted through GSM/LTE communication and visualized using a web-based monitoring platform. The results indicate that the system successfully performs real-time data acquisition, storage, visualization, and air quality classification based on Indeks Standar Pencemar Udara (ISPU) standards. The implementation of Mamdani Fuzzy Logic shows that air quality conditions were predominantly categorized as Good, with the highest ISPU value of 67 recorded at 12:00 due to increased PM2.5 and PM10 concentrations. The findings demonstrate that the proposed IoT-based monitoring system can provide an effective, automated, and structured solution to support air quality inspection processes. Future improvements are recommended through sensor calibration, the addition of environmental parameters, and integration with predictive intelligence models.