<![CDATA[This research aims to develop a Flask-based Smart Library system integrated with face recognition technology, RFID, and automated WhatsApp notifications to support library services at BBPPMPV BOE Malang. The face recognition system employs two methods: Local Binary Pattern Histogram (LBPH) and Convolutional Neural Network (CNN) for comparison. Testing results show that the LBPH method achieved a training accuracy of 98.77%, but its real-world recognition accuracy dropped to 39.77%. In contrast, the CNN method achieved a lower training accuracy of 69.11% but demonstrated more stable performance in real conditions with an average accuracy of 76.87%. RFID technology is implemented to automate the book borrowing and returning process through website integration, resulting in a time efficiency improvement of up to 74% compared to manual systems. Additionally, the system features real-time notification via WhatsApp using Venom Bot, which successfully delivers book transaction details accurately and consistently with the database records. The system is built using a Raspberry Pi 4 and the Flask framework and is accessed through a web-based interface. The implementation results show that the system significantly enhances the efficiency, security, and convenience of library services.]]>
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