<![CDATA[Dehydration is a condition in which the body loses more fluids than it receives, potentially causing health problems and disrupting normal body functions. One indicator of dehydration can be observed through urine color, where darker urine indicates a higher level of dehydration. This study aims to implement the K-Nearest Neighbor (KNN) algorithm in an Internet of Things (IoT)- and Android-based dehydration detection system using urine color analysis. The system utilizes a TCS3200 color sensor to detect urine color, an ESP32 microcontroller to process and transmit data, and an Android application to display detection results in real time. The acquired color data are processed using the KNN algorithm to classify dehydration levels into several categories. The classification results are then displayed on the Android application along with hydration reminder notifications. System testing was conducted using 50 artificial urine color samples with varying color intensities. The results showed that the proposed system was able to classify dehydration levels with an accuracy of 92% and successfully display real-time detection results through the Android application. The developed system is expected to assist users in monitoring their hydration status easily and effectively.]]>
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