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IoT-Based Road Blackspot Detection via GPS and Web Integration: Design, EAN-Based Risk Classification, and Field Evaluation Rahmatullah, Ghani Ridho; Tsani, Mokhammad Rifqi; Pratindy, Raka; Shofiah, Siti
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.267

Abstract

Purpose – Road safety on high-traffic inter-city corridors in Indonesia remains a pressing concern, as drivers receive no real-time hazard notification when approaching zones with statistically elevated crash history. This study develops and evaluates an ESP32-based early warning system that couples GPS-derived positioning with the Equivalent Accident Number (EAN) method to issue graduated audio-visual alerts at road blackspots along the Palur–Semarang bus corridor. Design –  EAN quantifies accident severity by weighting fatalities (12), serious injuries (3), minor injuries (1), and property-damage incidents (0.5); segments exceeding the Upper Control Limit (UCL = 170,52) are designated blackspots, with coordinates stored in onboard flash memory. A SIM800L GPRS module transmits positioning data to a web-based fleet monitoring dashboard. Findings – Field evaluation across 10 GPS sampling points yielded mean errors of 0.00033% for latitude (3.7 m) and 0.00005% for longitude (5.0 m), with maximum deviations of 8.9 m and 17.8 m—both within the 800 m geofencing radius. All 10 from 64 validated corridor zones returned EAN values of 199,5–668,5, each exceeding the UCL, with web-platform outputs matching manual calculations exactly. Eight integrated test scenarios confirmed three-tier audio-visual alert delivery at 800 m, 400 m, and 100 m thresholds with zero missed triggers and zero spurious activations. Research implications – These findings provide preliminary evidence for the technical feasibility of EAN-based blackspot intelligence as a driver vigilance aid; however, full-route longitudinal testing across diverse vehicles and network conditions is required before generalised deployment can be recommended. Originality – This study integrates EAN-based crash severity analysis with real-time GPS tracking in an ESP32 system to deliver tiered early warnings for road blackspots.  
The Smart Battery Safety and Anti-Theft Monitoring System for Electric Bicycles with Automatic Cut-Off and Dual-Channel Notification Perdana, Andhika Putra; Tsani, Mokhammad Rifqi; Wibowo, Helmi; Widiandaru, Nanang Okta
Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v3i2.685

Abstract

The rapid growth of electric bicycle usage in Indonesia has been accompanied by rising safety incidents, particularly those related to battery thermal runaway and theft. This research presents the design and implementation of an integrated monitoring and security system for electric bicycles using the ESP32 microcontroller, PZEM-017, DS18B20, and Neo-6M GPS module, combined with a web-based dashboard and Telegram bot notification. The system was developed using the Research and Development (R&D) method with a four-parameter monitoring scheme covering voltage, current, temperature, and geospatial coordinates. Experimental results from twenty data points per sensor demonstrated excellent accuracy: DS18B20 achieved an average error of 1.133%, PZEM-017 achieved 1.224% for voltage and 1.787% for current, while the Neo-6M module achieved 0.000575% and 0.000042% for latitude and longitude respectively. The automatic cut-off mechanism successfully operated in all six tested scenarios, and the Telegram-website integration delivered notifications with an average delay of two seconds. These findings confirm that the proposed system improves safety and security of electric bicycles through real-time multi-parameter monitoring and remote intervention capability. Unlike prior systems that address monitoring or security in isolation, this work is the first to unify real-time multi-parameter battery protection, automatic cut-off, geofencing, and dual-channel notification within a single low-cost ESP32-based platform tailored for urban electric bicycle users in Indonesia. The practical relevance of this integration is particularly significant given the accelerating adoption of electric bicycles as primary short-distance transportation in densely populated Indonesian cities, where charging-related fire incidents and theft cases have reached critical levels.  
IoT-Based Dual-Sensor Vehicle Security System Using Piezoelectric and Glass-Break Detectors with GPS Tracking: Design and Performance Evaluation Rohdyawan, Zaidan Wafi; Hakim, M. Iman Nur; Pratindy, Raka; Tsani, Mokhammad Rifqi
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.421

Abstract

Purpose – This study aims to develop an IoT-based vehicle security system using a dual-sensor architecture that integrates piezoelectric and glass-break sensors, complemented by GPS tracking, to detect glass-break–based theft attempts rapidly, accurately, and in real time.Methods – A Research and Development (R&D) approach was employed at the functional prototype stage, involving hardware design, ESP32 programming, vibration response testing, glass-break tests on tempered and tinted glass, GPS accuracy assessment across three environmental conditions, and validation of IoT notification response time via Telegram.Findings - An IoT-based vehicle security system integrating piezoelectric and glass-break sensors demonstrated clear signal separation between normal conditions (ADC < 500) and glass-break events (ADC > 1000), with no overlapping distributions observed during testing. The system achieved real-time detection with an average IoT notification response time of approximately 1.14 seconds and showed near-zero false alarm occurrence under controlled experimental conditions.Research implications – Although the prototype exhibits high sensitivity and specificity in controlled environments, system performance remains influenced by IoT network quality and GPS signal degradation in enclosed spaces. Testing was limited to one vehicle model and two glass types; therefore, further research is required, including large-scale field validation, evaluation in dynamic environments, and the implementation of advanced IoT security protocols.Originality – The main contribution of this study lies in addressing the research gap between prior works that predominantly utilized single-sensor or limited sensor combinations without robust acoustic–mechanical differentiation. The applied integration of piezoelectric and glass-break sensors within an IoT-based architecture establishes a cross-verification mechanism that significantly reduces false alarm potential and enhances detection reliability compared to previous approaches.