<![CDATA[Dehydration frequently goes unnoticed due to its gradual onset, posing a risk of serious clinical complications if left unaddressed. This study aims to develop an Internet of Things (IoT)-based prototype for objective and real-time early dehydration detection through multi-parameter urinalysis, including color (RGB), pH, and temperature. The methodology encompasses user and medical needs analysis, instrument system design utilizing the NodeMCU ESP32, and validity testing using 10 real urine samples cross-validated directly against the official diagnosis (gold standard) from the Cibinong Regional Health Office, Bogor Regency. Quantitative results demonstrate that the system achieves an overall classification accuracy of 80% and a sensitivity of 100% in extreme conditions (No Dehydration and Severe Dehydration). A minor deviation of 20% occurred during transition phases due to optical sensor sensitivity toward ambient lighting variations and non-hydration chemical fluctuations in the urine. The novelty of this research lies in the integration of a temperature parameter to mitigate diagnosis bias caused by febrile (fever) conditions, alongside the implementation of an expert system on the web interface capable of transforming raw sensor data into personalized disease risk predictions and nutritional recommendations. This system contributes a practical, non-invasive, and self-managed instrument for preventive health monitoring.]]>
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