<![CDATA[Most Internet of Things (IoT)-based environmental monitoring systems primarily present raw sensor data, requiring users to interpret environmental conditions manually. This research focuses on developing a web-based IoT monitoring system for temperature and humidity, incorporating a Mamdani Fuzzy Logic Inference System (FIS) to interpret environmental conditions more effectively and support informed decision-making. The proposed system's novelty lies in integrating real-time IoT monitoring with fuzzy inference, enabling evaluation based on gradual transitions in temperature and humidity rather than rigid threshold boundaries. The system was implemented using an ESP32 microcontroller, a DHT22 sensor, a MySQL database, and a web-based dashboard. System evaluation included functional testing, end-to-end latency measurement, and continuous monitoring to assess system uptime and transmission performance. The functional testing confirmed that all major system modules operated successfully. The system achieved an average end-to-end latency of 230.02 ms across 50 consecutive transmissions and 100% uptime during an 8-hour continuous monitoring period, with 4,954 recorded transmissions and an average transmission interval of 5.81 s. The results show that the proposed system provides responsive, continuous environmental monitoring while integrating fuzzy-based environmental assessment to support more informed decision-making. Keywords – Internet of Things, Environmental Monitoring, Web Monitoring, Mamdani Fuzzy Logic, Decision Support]]>
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