Performance Analysis of a Smartphone-Controlled Smart System for Motorcycle Starting and Security Functions - Wabdillah Wabdillah; Muhammmad Yahya; Muhammad Iskandar Musa; Syakia Muflihat; Alfred Dalla (Jurusan Pendidikan Teknik Elektro, 2025)
Motorcycle starting failures and theft vulnerabilities remain significant issues in many developing regions, primarily owing to weaknesses in mechanical locking systems and inconsistent electrical performance. To address these challenges, this study developed and evaluated a smartphone-controlled smart system that integrates motorcycle starting and security functions using Bluetooth and Wi-Fi communication. This research aims to quantitatively assess the system’s responsiveness, communication stability, energy efficiency, and security performance under both laboratory and real-world operating conditions gaps that remain insufficiently explored in previous studies. The system was built using an ESP32 microcontroller equipped with AES-128 encrypted communication, a relay-based ignition interface, and an intrusion-detection module controlled through a dedicated mobile application. A series of 100 command trials were conducted to measure the latency, functional reliability, wireless transmission stability, power consumption, and alarm response time. The results show that Wi-Fi mode provides faster command execution, with response times consistently lower than Bluetooth mode, while Bluetooth demonstrates higher communication stability and lower energy usage. Both communication modes achieved operational accuracy above 97%, and the security subsystem responded rapidly to intrusion events, activating alarms in approximately 1.3 seconds. These findings indicate that the proposed system achieves a balanced performance across speed, reliability, and energy efficiency, validating its suitability for practical motorcycle automation and security applications. The primary contribution of this study lies in its comprehensive quantitative evaluation framework, which offers deeper empirical insight into IoT-based vehicle control performance. The results also provide a foundation for future advancements, including cloud-integrated monitoring, GPS-based tracking, and AI-driven predictive security.