<![CDATA[Volcanoes are geological phenomena that can cause significant disasters to human life and the environment, such as eruptions, pyroclastic flows, and lahars. Therefore, early warning systems for volcanoes are crucial to reduce disaster risks and provide sufficient time for evacuation. Monitoring surface temperature and the surrounding air around volcanoes is one of the key parameters in detecting volcanic activity. Temperature increases often serve as an early indication of magmatic activity beneath the surface. This study proposes an early warning system for volcanoes based on temperature sensors integrated with fuzzy logic to monitor volcanic activity in real-time. The system consists of a wireless temperature sensor network based on the Internet of Things (IoT) connected to an IoT platform for data monitoring and analysis. The SHT31D, SHT2X, BME280 and DHT11 sensors are used to measure the ambient temperature, and the temperature data is processed using fuzzy logic methods to detect changes in volcanic activity. The system was tested in both simulation and field environments using sensor node devices consisting of several temperature sensors controlled by a microcontroller. The fuzzy logic algorithm built using 256 rules is able to classify new data from sensor nodes into one of the categories of volcano vulnerability levels, namely “Normal”, “Caution”, “Warning”, or “Evacuate”. This system has the potential to serve as a real-time temperature monitoring tool for volcanoes, supporting disaster mitigation and volcanic activity risk management.]]>
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