<![CDATA[The development of the Internet of Things (IoT) has encouraged the implementation of automation systems in various fields, including photobooth services. This study aims to design an automatic photo capture control system for Deebooth Photobooth by integrating a sound sensor, OpenCV, and the Fuzzy Mamdani method. An ESP32 microcontroller is used to acquire sound intensity data, while OpenCV with the Haar Cascade method is employed to detect human objects within the photo capture area. Sound intensity data and frame validation results are processed using Fuzzy Mamdani through fuzzification, inference, and defuzzification stages to generate photo capture decisions. The results show that the sound sensor can distinguish sound intensity levels under different environmental conditions, while OpenCV successfully detects human objects in real time. The implementation of Fuzzy Mamdani produces three output categories: No Capture, Standby, and Capture. The system performs automatic photo capture only when a human object is detected and the sound intensity satisfies the predefined fuzzy rules. The proposed system improves the flexibility and reliability of automatic photo capture in IoT-based photobooth applications.]]>
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