Implementation of Automated Attendance System Using Fingerprint with Wemos D1 R32 and WhatsApp Notifications

The manual attendance system often leads to inefficiencies, such as input errors, delayed data recap, and slow delivery of information to parents. This study aims to design and implement an automated attendance system based on the Internet of Things (IoT) using the AS608 fingerprint sensor and Wemos D1 R32 microcontroller integrated with attendance notifications via WhatsApp. The research method includes the stages of design, implementation, and testing using the black box approach and system performance evaluation. The results show that the system functions properly, successfully reads fingerprints, displays attendance information on the LCD, and sends WhatsApp notifications with an average response time of under 3 seconds. Black box testing confirmed that all main features, such as login, registration, fingerprint verification, and data export, work as expected. The sensor test results indicate a very high success rate under normal finger conditions but decreased accuracy when the finger is dusty, wet, or wrinkled. Overall, this system improves the efficiency, accuracy, and transparency of the student attendance process and is ready for implementation in a school environment.