<![CDATA[Concentration is a crucial mental focus ability that significantly impacts learning effectiveness and work productivity. Decreased concentration levels can be caused by various factors such as fatigue, stress, environmental disturbances, or specific psychological conditions. Poor concentration affects performance quality and task completion efficiency. Therefore, early detection of concentration levels can help optimize learning processes and improve overall performance. This research designs a concentration level detection system based on physiological sensors using DS18B20 temperature sensor to read body temperature data, pulse sensor to measure heart rate, and GSR sensor to monitor skin conductivity levels. These three physiological parameters serve as inputs for concentration level detection using the certainty factor method with output concentration levels consisting of highly focused, normally focused, less focused, and unfocused. The system design employs System Development Life Cycle (SDLC) Waterfall model approach encompassing requirement analysis, system design, and testing methodology. The system is designed to provide real-time concentration level detection based on physiological parameter changes during cognitive activities. This research produces a system design that serves as a foundation for developing concentration monitoring tools in various applications.]]>
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