Medan, September 11, 2026 — A team of lecturers from the Diploma Program in Metrology and Instrumentation, Faculty of Vocational Education, Universitas Sumatera Utara (USU), has successfully completed the research and development of the TOBA Helmet prototype: Technology Onboard for Bike Automation, a smart helmet based on the Internet of Things (IoT) designed to reduce the risk of motorcycle robbery against riders. The research was led by Dona Tiara Lubis, S.T., M.T. as the principal researcher, together with three team members, namely Junedi Ginting, S.Si., M.Si., Lukman Hakim, S.Si., M.Si., and Syukratun Nufus, S.Si., M.Sc., under the 2025 USU Talent Young/Beginner Lecturer Research Scheme (Pioneer Research).

This research originated from the team’s concern over the high incidence of motorcycle robbery in Medan. Motorcycle theft cases were recorded at 184 incidents in 2021, increasing to 209 cases in 2022, and rising sharply to 399 reported robbery cases by mid-2023, although only a small portion were successfully resolved by law enforcement. Vehicle security systems available on the market generally rely on a single device installed on the motorcycle, making them less responsive when riders are forced off or separated from their vehicles during a robbery. This issue led to the development of the TOBA Helmet concept: providing a security system that “moves” with the rider rather than being attached solely to the vehicle.

Technically, the TOBA Helmet operates through two ESP32 microcontroller modules that are wirelessly connected via Wi-Fi/Bluetooth—one installed inside the helmet and the other on the vehicle. The motorcycle module is equipped with a NEO-6M GPS module for real-time location tracking, dual relays to control the electrical flow to the engine and horn, and a custom PCB to make the device more compact. As long as the helmet remains within the motorcycle’s signal range, the engine operates normally. However, when the distance between the helmet and motorcycle exceeds a safe radius of approximately 2 meters—for example, when the rider is forced off the motorcycle by a perpetrator—the system automatically cuts off the electrical supply to the engine and activates the alarm, while the vehicle’s location coordinates are transmitted and can be monitored in real time through the Blynk application.

The assembled prototype was tested in five types of environments in Medan—open fields, residential areas, urban areas, congested roads, and quiet roads. The results showed that the system consistently maintained a connection at a distance of up to 2 meters between the helmet and vehicle, with an engine shutdown response time of 20–63 seconds depending on environmental interference. The GPS module was also able to accurately track the vehicle’s location, although satellite signal acquisition times varied between 180–900 seconds depending on building density. In terms of endurance, the helmet module was able to operate for approximately 27 hours using a 3000 mAh Li-ion battery.

In addition to producing a functional prototype, the research resulted in several scientific outputs. An article entitled “TOBA Helmet Technology Onboard for Bike Automation to Reduce the Risks of Violent Motorcycle Crimes” has been accepted (Letter of Acceptance) for presentation at The 5th International Symposium on Materials and Electrical Engineering (ISMEE 2025) at Universitas Pendidikan Indonesia, Bandung, and will be published in IEEE Xplore, which is indexed by Scopus. The research team also produced digital teaching materials in the form of a system documentation video published through a YouTube channel.

The research also involved three students from the Diploma Program in Metrology and Instrumentation in the prototype development process, while recognizing 14 credits (SKS) of coursework as a form of research integration into learning in line with the spirit of Merdeka Belajar Kampus Merdeka. Going forward, the team plans to further develop the system, ranging from optimizing data communication and integrating geofencing to applying machine learning to detect riding behavior, as outlined in the research roadmap through 2029. This research also supports Sustainable Development Goal (SDG) 9: Industry, Innovation, and Infrastructure through the strengthening of technology-based public safety infrastructure.