Implementation of Fuzzy Tsukamoto Algorithm on Smart Node Sensors for Air Quality Monitoring

In this modern era, there are more and more industrial centers in urban areas as well as the development of various transportation. This is causes air pollution that can affect human, animal, plant life. The initial study of the application of the Fuzzy Tsukamoto algorithm on smart sensor node system for air quality monitoring described in this paper. The air parameters used are CO, CO2, and CH4. The data used were obtained from previous studies. This data is used to test the accuracy of the fuzzy model using two rule bases and two types of fuzzy. The results show that the accuracy value of Fuzzy Tsukamoto using the second rule base is better, namely 92.4 percent.The classification results are in the form of warning alerts that are combined in one frame with other data on the raspberry pi. The data are sent to the PC server using LoRa transceivers. From indoor and outdoor testing, the results show that the system can work well. In indoor conditions, the results of the air classification are Good with CO concentrations of not more than 0.9 ppm, CO2 concentrations of 293-402 ppm and CH4 concentrations of 0.5 ppm. In outdoor conditions, the results obtained are Moderate air classification with CO concentrations of not more than 1.0-1.1 ppm, CO2 concentrations of 707-915 ppm, and CH4 concentrations of 1.62.3 ppm.