Data Quality in an IoT Sensor System for Water Quality: Demonstrated on Time-Dependent Water Temperature Fluctuations - Jaja, Jaja; Surya, Irgi; Fahrizal, Diki (Gunung Djati Conference Series, 2025)
The increasing demand for reliable and efficient Internet of Things (IoT) applications underscores the importance of ensuring high-quality sensor data for accurate monitoring and decision-making. This study focuses on data quality in an IoT sensor system for water quality monitoring, specifically demonstrated on time-dependent water temperature fluctuations. The system integrates fuzzy logic–based analysis and IoT connectivity using the Adafruit MQTT platform to enable real-time data acquisition, monitoring, and anomaly detection through smartphones, tablets, or computers. To ensure systematic development, the research employs the ADDIE methodology (Analysis, Design, Development, Implementation, Evaluation), enabling iterative refinement of both hardware and software components. Experimental results show that the system effectively captures temperature variations over time while identifying anomalies that may indicate sensor drift, environmental irregularities, or potential system faults. By addressing both measurement reliability and anomaly detection, this research contributes to improving data quality in IoT-based water monitoring systems, providing a scalable solution for sustainable water resource management and industrial applications/