Theopilus S.P Sibarani, Eko Setia Budi, Abdul Rahman Kadafi

Implementasi dan Optimasi Sistem Monitoring Kualitas Udara Berbasis Sensor Gas MQ-135 dan SHT21 dengan Metode IoT

Introduction

Implementasi dan optimasi sistem monitoring kualitas udara berbasis sensor gas mq-135 dan sht21 dengan metode iot. Implementasi & optimasi sistem monitoring kualitas udara berbasis IoT dengan sensor MQ-135 & SHT21. Mendeteksi gas berbahaya, suhu, kelembaban real-time, notifikasi dini, dan kalibrasi akurat.

Abstract

The decline in air quality caused by industrialization and urbanization requires an automated, real-time, and affordable monitoring method, since manual measurement by environmental health officers is still limited to periodic site visits. This study aims to implement and optimize an air quality monitoring system based on the Internet of Things (IoT) using the MQ-135 gas sensor and the SHT21 temperature-humidity sensor, targeting improved gas-sensor calibration accuracy through linear regression and faster, more reliable data transmission to a MySQL database. The system is designed to detect environmental parameters such as hazardous gas concentrations, temperature, and humidity, which are then transmitted in real-time to a web-based platform for visualization and data analysis. The implementation process involves integrating hardware, including the Arduino Uno and NodeMCU ESP8266 microcontrollers with the MQ-135 and SHT21 sensors, as well as developing software that enables information processing and IoT communication. Testing was conducted to evaluate sensor accuracy, data transmission stability, and system reliability under varying environmental conditions. The results demonstrate that the system can provide accurate information and respond effectively to changes in environmental parameters, sending notifications when gas concentrations exceed predefined thresholds. This system not only serves as a monitoring tool but also as an educational medium to raise awareness of the importance of maintaining air quality. The main contribution of this research is a self-hosted air quality monitoring architecture that combines a quantitatively validated MQ-135 gas-sensor calibration method with an integrated web- and Telegram-based early-warning system. Further development opportunities, such as incorporating predictive analytics and AI for more accurate air quality data analysis, emerge as outcomes of this research.

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