Automated Attendance and Analytical System using QR Code And Mern Stack
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Abstract
This paper presents the design and implementation of an Automated Attendance and Analytical System that leverages QR code technology integrated with the MERN stack (MongoDB, Express.js, React.js, Node.js). Traditional manual attendance methods are time-consuming, error-prone, and lack real-time analytical insights. The proposed system enables students to mark attendance by scanning dynamically generated, session-bound QR codes, while administrators and faculty gain access to a real- time analytics dashboard. The frontend is built using React.js and Tailwind CSS, ensuring a responsive and intuitive interfac e across devices. The backend is powered by Node.js and Express.js, with MongoDB serving as the primary database. A pilot conducted over six weeks with 120 students demonstrated 99.2% attendance recording accuracy, an average scan response time of 142 ms under concurrent load, and significant reduction in administrative overhead. The results confirm the viability of the proposed system as a scalable, hardware-free replacement for conventional attendance processes
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