IoT Based Industrial Waste Monitoring System Design with Data Visualization on A Web Application Using The Supervised Learning Method - Ritonga, Muhammad Nauval Asyqar Ridwan; Maulana, Halim (Hanif Journal of Information Systems, 2025)
Industrial waste management is a critical aspect of sustainable manufacturing, as improper handling can lead to severe environmental pollution and health hazards. Real-time monitoring of industrial waste parameters enables early detection of irregularities and supports informed decision-making for compliance with environmental regulations. This study presents the design of an IoT-based industrial waste monitoring system integrated with data visualization on a web application and enhanced by the supervised learning method for predictive analysis. The system utilizes IoT sensor nodes to measure key waste parameters such as pH level, temperature, turbidity, and chemical concentration. Sensor data is transmitted wirelessly to a cloud server, where it is stored, processed, and analyzed using supervised learning algorithms to classify waste quality and detect potential violations. The web application provides interactive dashboards, historical data tracking, and real-time alerts for stakeholders. Testing results demonstrate that the system achieves high accuracy in classifying waste conditions, offers user-friendly visual analytics, and enables proactive waste management. This research contributes to the development of intelligent environmental monitoring solutions, promoting efficiency, compliance, and sustainability in industrial operations.