| Issue |
BIO Web Conf.
Volume 144, 2024
1st International Graduate Conference on Smart Agriculture and Green Renewable Energy (SAGE-Grace 2024)
|
|
|---|---|---|
| Article Number | 03004 | |
| Number of page(s) | 9 | |
| Section | Environmental Monitoring and Water Management | |
| DOI | https://doi.org/10.1051/bioconf/202414403004 | |
| Published online | 25 November 2024 | |
Real-Time Monitoring System for CO Pollutant Concentration Using Fuzzy Logic on an Internet of Things (IoT) Platform and Telegram Around UNNES
1 Applied Physics Study Program, Faculty of Mathematics and Natural Sciences, Semarang State University, Sekaran Campus Gunungpati, Semarang 50229, Central Java, Indonesia.
2 Geography Study Program, Faculty of Social and Political Sciences, Semarang State University, Sekaran Campus Gunungpati Semarang 50229, Central Java, Indonesia
3 Out-of-School Education Study Program, Faculty of Education and Psychology, Semarang State University, Sekaran Campus Gunungpati, Semarang 50229, Central Java, Indonesia.
4 Physics Education Study Program, Faculty of Mathematics and Natural Sciences, Semarang State University, Sekaran Campus Gunungpati, Semarang 50229, Central Java, Indonesia.
5 Public Health Study Program, Faculty of Medicine, Semarang State University, Sekaran Campus Gunungpati, Semarang 50229, Central Java, Indonesia
* Corresponding author: g4lihru@students.unnes.ac.id
The growth of the population and vehicles around the campus of Universitas Negeri Semarang (UNNES) has created new challenges related to traffic congestion and air quality. High traffic density can produce exhaust emissions, especially carbon monoxide (CO). CO is a toxic gas that comes from burning fossil fuels, such as those produced by motor vehicles. Long- term exposure to CO can cause serious health problems, including respiratory problems, cardiovascular disease, and even death. To address these issues, a real-time CO concentration monitoring system is required. However, the current CO concentration monitoring system has limitations in terms of accuracy and precision. The Fuzzy Logic (FL) method offers several advantages, including high accuracy and ease of comprehension. To implement this, research stages are necessary, including tool coding and data analysis using FL. The results of the research indicate that CO levels in each UNNES Faculty are approximately 3-20 ppm, with CO levels stabilising at 6 ppm. Therefore, the CO monitoring system using FL was successfully implemented.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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