Iqbal Firdaus, Yusuf Wisnu Mandaya, Muhammad Yunan, Novia Urfiyati, Maisarah Maisarah, Yeni Agus Nurhuda, Naufal Ilham

Smart Classroom Monitoring System Using IoT and AI to Improve Space Utilization in Higher Education

Introduction

Smart classroom monitoring system using iot and ai to improve space utilization in higher education. Optimize higher education classroom space and energy with an IoT & AI Smart Monitoring System. Real-time occupancy detection & AI analysis boost utilization, scheduling, & sustainability.

Abstract

The rapid advancement of digital technology has encouraged higher education institutions to implement intelligent systems for improving facility management efficiency. This study focuses on the development and evaluation of a Smart Monitoring Class system that integrates Internet of Things (IoT) and Artificial Intelligence (AI) technologies to optimize classroom utilization. The system employs multiple sensors to monitor environmental parameters such as temperature, humidity, and illumination, as well as to detect occupancy in real time. The collected data are processed using AI algorithms to analyze room usage patterns and provide recommendations for scheduling and energy management. The research follows the System Development Life Cycle (SDLC) approach, including stages of requirement analysis, system design, implementation, and evaluation. Experimental results show that the proposed system achieves high performance in terms of sensor accuracy, data transmission delay, and overall system reliability. The AI module successfully identifies classroom usage patterns with an accuracy rate of over 90%, enabling adaptive control of environmental conditions and energy-saving mechanisms. These findings indicate that the integration of IoT and AI technologies significantly enhances the efficiency, transparency, and sustainability of classroom management in higher education institutions.

Review

This paper addresses a highly pertinent challenge in modern higher education: optimizing space utilization and facility management through technological innovation. The authors present a timely and relevant study on the development and evaluation of a Smart Classroom Monitoring System. By effectively integrating Internet of Things (IoT) sensors for real-time environmental and occupancy data collection with Artificial Intelligence (AI) algorithms for data analysis, the research proposes a novel approach to enhance classroom efficiency, transparency, and sustainability. This work represents a significant step towards leveraging digital advancements to address practical operational needs within academic institutions, offering a promising solution to improve resource allocation. The methodology adopted for this study follows a structured System Development Life Cycle (SDLC), encompassing essential stages from requirement analysis to system evaluation. The core of the proposed system lies in its dual technological backbone: a network of IoT sensors deployed to capture crucial environmental parameters like temperature, humidity, illumination, and real-time occupancy, alongside AI algorithms designed to process this extensive dataset. The experimental evaluation demonstrates robust system performance, reporting high sensor accuracy, minimal data transmission delays, and overall reliability. Notably, the AI module achieved an impressive accuracy rate of over 90% in identifying classroom usage patterns, which is critical for enabling adaptive environmental control and implementing energy-saving mechanisms. These findings convincingly support the system's capability to provide actionable insights for optimized resource allocation. While the presented results clearly highlight the significant potential of integrating IoT and AI for improving classroom management, the abstract could benefit from a discussion of scalability challenges or cost implications for widespread deployment across diverse institutional settings. Furthermore, while usage pattern identification is effective, future work could explore predictive modeling capabilities, perhaps incorporating external factors like academic calendars or student enrollment data for even more sophisticated scheduling recommendations. Despite these considerations, the study provides a compelling proof-of-concept for intelligent facility management in higher education, laying a strong foundation for future research into adaptive and sustainable campus operations.

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