What question did this study set out to answer?

The aim is to develop a smart library management system that automates traditional operations using advanced technologies.

July 1, 2026

Libraria: An Intelligent Smart Library Management System Using Machine Learning, R Analytics, and IoT-Based QR Verification

Key Points

The aim is to develop a smart library management system that automates traditional operations using advanced technologies.
Developed an integrated digital platform using web development, machine learning, and IoT technologies.
Implemented QR-based verification for secure borrowing and authentication.
Utilized R programming for statistical analysis such as borrow trend analysis and overdue risk prediction.
Increased efficiency in library operations by automating manual processes.
Improved user experience through personalized book recommendations and real-time analytics.
Enabled contactless authentication with QR code technology, enhancing security.

Abstract

The Libraria Smart Library Management System is an integrated digital platform developed to automate and modernize traditional library operations using Web Development, Machine Learning, R Analytics, Mobile Application Development, and IoT-based QR verification technologies. Conventional library systems often depend on manual processes for maintaining records, verifying borrow requests, calculating fines, and tracking inventory, leading to inefficiency and increased administrative workload. The proposed system addresses these limitations by providing an intelligent, secure, and user-friendly smart library platform. The system enables students and administrators to access library services through both web and Android mobile applications. The frontend was developed using HTML, CSS, and JavaScript, while Supabase was used as the cloud backend for authentication, database management, and real-time synchronization. The system supports functionalities such as secure login, book searching, borrow request management, QR-based verification, fine tracking, analytics generation, and personalized recommendations. Machine Learning techniques such as TF-IDF and Cosine Similarity were implemented to generate intelligent book recommendations based on user borrowing history and preferences. R programming was used to generate statistical insights including borrow trend analysis, overdue risk prediction, correlation analysis, and fine statistics. QR code technology was integrated to automate borrowing verification and improve security through contactless authentication. The project demonstrates the practical integration of multiple modern technologies into a single unified platform capable of improving automation, accessibility, analytics, and user experience in educational institutions. Overall, Libraria provides a scalable and efficient smart library solution suitable for modern digital learning environments.

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