Development of a Raspberry Pi-Based IoT Smart Laboratory Trainer with an I2C Display and Solenoid Lock Actuator for the Telecommunications Laboratory of the Faculty of Engineering, UNESA

Abstract

Aims: This study aimed to design, develop, and assess a Raspberry Pi-based Internet of Things (IoT) Smart Laboratory Trainer Kit incorporating flame and gas sensors, RFID authentication, an I2C LCD display, a relay-operated solenoid lock, and Telegram Bot communication to support practical IoT education in the Telecommunications Laboratory at the Faculty of Engineering, Universitas Negeri Surabaya. Study Design: Research and Development (R&D) using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) framework. Location and Duration of Study: The study was conducted in the Telecommunications Laboratory, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia, over 11 months. Methodology: The trainer was constructed using a Raspberry Pi as the primary controller and Python programming to support sensing, visualisation, authentication, automation, and remote communication. Functional testing assessed system performance and integration, while educational usability was evaluated using a four-point Likert-scale questionnaire completed by 15 student validators. Results: Functional testing confirmed the operation of all modules, including flame and gas detection, RFID authentication, I2C LCD visualisation, relay-controlled solenoid-lock actuation, and Telegram Bot notifications. The trainer demonstrated a complete IoT workflow by integrating sensing, embedded processing, visualisation, intelligent actuation, and remote communication within a single training platform. The user evaluation yielded an average score of 3.76 out of 4.00, indicating high acceptance as an interactive learning platform. Conclusion: The developed trainer provides an integrated platform for IoT education that supports students' practical skills in embedded systems, automation, and smart laboratory applications, while offering a scalable foundation for future Industrial IoT learning.