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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Dzaki Putra Prakosa
Politeknik Keselamatan Transportasi Jalan
Computer Science & IT

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Real-Time Intelligent IoT-Based Drum Brake Wear Monitoring System Dzaki Putra Prakosa; Setya Wijayanta
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2609

Abstract

Purpose – This study aims to develop and evaluate a real-time IoT-based intelligent monitoring system for drum brake lining wear to overcome the limitations of conventional manual inspection, which is periodic, subjective, and prone to delayed detection of critical wear conditions. Design/methods/approach – The research adopts a Research and Development (R&D) methodology consisting of design, prototyping, and laboratory testing. The system integrates an ESP32 microcontroller with a VL53L0X time-of-flight sensor to measure brake lining thickness in real time. A rule-based classification algorithm is implemented to categorize brake conditions into SAFE, WARNING, and DANGER states. Experimental evaluation was conducted through 15 trials across a thickness range of 1.0–10.0 mm. Performance metrics include accuracy, mean absolute error (MAE), root mean square error (RMSE), and response time. Findings - The system achieved an average measurement accuracy of 96.0%, MAE of 0.13 mm, and RMSE of 0.15 mm. All samples were correctly classified, resulting in 100% classification accuracy across the three condition states. The system also recorded a mean response time of 1.27 seconds, indicating fast and reliable real-time performance under controlled conditions. Research implications/limitations – The system is feasible for low-cost brake wear monitoring, but validation is limited to laboratory conditions with a small dataset, and real-world factors were not examined. Originality/value – This study presents an integrated IoT-based drum brake monitoring framework combining ToF sensing, embedded rule-based intelligence, and mobile notification in a single low-cost system. It specifically addresses drum brake applications, which remain underexplored compared to disc brake monitoring systems, offering a practical solution for resource-constrained environments.
Real-Time Ambulance Detection System at Traffic Intersections Using Raspberry Pi and YOLOv5 Muhammad Isro’ Risqi; Raka Pratindy; Dzaki Putra Prakosa
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2635

Abstract

Purpose – Delays experienced by ambulances at signalized intersections remain a critical issue in emergency transportation, particularly in dense urban traffic conditions. This study aims to develop and evaluate a low-cost real-time ambulance detection system using Raspberry Pi 4B and YOLOv5 to support intelligent transportation monitoring and emergency vehicle prioritization. Design/methods/approach – This study employed an experimental research design by integrating CCTV cameras, Raspberry Pi 4B, YOLOv5s object detection, ONNX Runtime INT8 optimization, and Telegram Bot API notification. The model was trained using 3,250 annotated ambulance images divided into training, validation, and testing subsets. System performance was evaluated under five operational scenarios: daytime, nighttime, heavy traffic, long-distance detection, and low-lighting conditions. Findings – The proposed YOLOv5s model achieved precision of 95.4%, recall of 93.8%, mAP@0.5 of 96.1%, and sustained throughput of 22 FPS on Raspberry Pi 4B. The Telegram notification subsystem achieved a transmission success rate of 98.7% with an average delay of 1.8 seconds. However, detection performance decreased under low-lighting conditions, with a true positive rate of 78.5% and false positive rate of 11.2%. Research implications/limitations – The system demonstrates the feasibility of deploying embedded computer vision for cost-effective ambulance detection, although nighttime reliability and traffic signal integration require further improvement. Originality/value – This study contributes an ONNX INT8-optimized YOLOv5s implementation on Raspberry Pi 4B with multi-condition evaluation and real-time Telegram notification for ambulance detection at traffic intersections.
Design and Implementation of an IoT-Based Multi-Parameter Radiator Coolant Quality Monitoring System for Predictive Maintenance Habib Roviurrahman; Buang Turasno; Dzaki Putra Prakosa; Ramadhan Dwi Prasetyo
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.12799

Abstract

Purpose – This study develops and evaluates an Internet of Things (IoT)-based multi-parameter monitoring system for assessing radiator coolant quality and supporting predictive maintenance. The system continuously monitors coolant volume, pH, temperature, and turbidity while providing real-time status information and anomaly alerts through a mobile application. Design/methods/approach – The study employed a Research and Development approach involving system design, prototype development, sensor calibration, laboratory validation, and field implementation. The prototype integrated an ESP32 microcontroller with ultrasonic, pH, temperature, and optical sensors. Sensor data were transmitted through Firebase Realtime Database and displayed using a mobile application developed with MIT App Inventor. Validation was performed against calibrated reference instruments, followed by field testing under urban driving conditions and usability evaluation using the System Usability Scale. Findings – The proposed system demonstrated high measurement accuracy across all monitored coolant parameters and maintained reliable real-time communication between the embedded device and mobile application. Field testing showed that the system successfully identified coolant leakage, acidification, and excessive turbidity before noticeable vehicle-performance degradation occurred. The mobile application also demonstrated favorable usability, indicating that the monitoring interface was practical and accessible for users. Research implications/limitations – The proposed system demonstrates potential for supporting real-time coolant-condition monitoring and early maintenance intervention. However, validation was limited to one vehicle, a 14-day testing period, and urban driving conditions. Originality/value – This study integrates four coolant-quality parameters within a unified IoT architecture, offering a more comprehensive predictive-maintenance approach than conventional single-parameter radiator monitoring systems.
Co-Authors Buang Turasno Habib Roviurrahman Muhammad Isro’ Risqi Raka Pratindy Ramadhan Dwi Prasetyo Setya Wijayanta