<![CDATA[In the digital era, the implementation of Internet of Things (IoT) technology continues to evolve and is utilized in various fields, including employee attendance systems. However, many non-ASN employees in government institutions still record their attendance manually by writing it down on paper. This conventional method has several weaknesses, such as the potential for fraud, inefficiency, and the inability to monitor attendance in real-time. To address these issues, this study aims to design and develop an IoT-based attendance system using the Waterfall method. The system integrates Face Recognition technology as the primary authentication method to ensure accuracy in attendance recording. The ESP32-CAM microcontroller is employed as the main device responsible for managing facial identification and transmitting data to the server. In addition, the system is equipped with a real-time notification feature through the Telegram application. The Waterfall method is applied to ensure that each stage of system development is carried out systematically, starting from requirements analysis, design, implementation, testing, and maintenance. The results of this study show that the developed attendance system improves efficiency, accuracy, and transparency in attendance management. With its implementation, non-ASN employees who previously relied on manual attendance can benefit from a more modern, practical, and reliable solution.]]>
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