WAQUAL: IoT-Based Integrated Water Turbidity Detection and Monitoring System to Improve Water Quality in Semarang - Utomo, Galih Ridho; Okta , Yang Ratu; Munawar , Maulana Dzaki; Fatimatuzzahro; Hasyim , Muhammad Fuad (Proceedings of Universitas Muhammadiyah Yogyakarta Graduate Conference, 2024)
Water pollution caused by agricultural waste is one of the most pressing environmental issues, particularly in developing countries where water sources are limited, water quality is often compromised. In Indonesia, water turbidity poses a threat to as many as 78% of 100.000 the population with liver cirrhosis. This study aims to develop an AI-based system for detecting and monitoring water turbidity to address the limitations of current systems, including imprecise detection and accuracy. The research employs the concept of drift in data representation and implementation by classifying data based on type. The research includes two stages: data analysis and AI methods. The results of this study demonstrate that the AI-based system has achieved an accuracy rate of 99.43%, detecting a turbidity level of 693502.5. The development of this AI-based system contributes to enhancing the reliability and effectiveness of water quality and resource management in agriculture. Further research is needed to optimize and validate the effectiveness of this AI-based system in other regions with similar problems. The implementation of this system could contribute to sustainable agriculture practices and better water resource management. By providing a more precise and accurate detection and monitoring system, this research can help to minimize the negative impact of water pollution caused by agricultural waste, which could improve human health and promote sustainable agriculture practices.