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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Ragam Pengabdian
Raka Pratindy
Politeknik Keselamatan Transportasi Jalan
Computer Science & IT Education Environmental Science Social Sciences Other

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Penerapan Catalytic Converter Berbahan Kuningan, Nikel, Dan Karbon Aktif Sebagai Upaya Penurunan Emisi Gas Buang Co Dan Hc Pada Mesin Bensin Pramudita Putri Audina Winasti; Moch. Aziz Kurniawan; Raka Pratindy; Aat Eska Fahmadi; Riza Phahlevi Marwanto
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus (Inprogress)
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/q6542y50

Abstract

Exhaust emissions from motor vehicles are a leading source of air pollution in urban areas, particularly carbon monoxide (CO) and hydrocarbon (HC) produced by incomplete combustion. This study examines the performance of a catalytic converter constructed from a composite combination of brass, nickel, and activated carbon materials in mitigating CO and HC emission levels produced by a Suzuki Carry Pick Up gasoline-powered vehicle. An experimental approach was used, measuring exhaust emissions before and after catalytic converter installation at five engine speeds—idle, 1500, 2000, 2500, and 3000 rpm—with three repetitions per condition. Measurements were taken using a calibrated gas analyzer at the Cilincing Vehicle Testing Unit, Jakarta. Results demonstrated that the catalytic converter reduced average CO emissions by 48.29%, from 0.343% to 0.177%, and HC emissions by 16.67%, from 152.4 ppm to 126.7 ppm. Higher engine speeds generally yielded lower emission levels, consistent with improved combustion efficiency as the air-fuel ratio (AFR) approached the stoichiometric value of 14.7:1. A temporary increase in emissions at 2500 rpm was attributed to valve overlap and combustion chamber pressure dynamics at mid-range speeds. These results confirm that brass and nickel as catalysts combined with activated carbon as an adsorbent represent an effective and economically accessible alternative to precious metals in catalytic converter fabrication.
IoT-Based Road Blackspot Detection via GPS and Web Integration: Design, EAN-Based Risk Classification, and Field Evaluation Ghani Ridho Rahmatullah; Mokhammad Rifqi Tsani; Raka Pratindy; Siti Shofiah
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.  
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.
Co-Authors Aat Eska Fahmadi Dzaki Putra Prakosa Ghani Ridho Rahmatullah Moch. Aziz Kurniawan Mokhammad Rifqi Tsani Muhammad Isro’ Risqi Pramudita Putri Audina Winasti Riza Phahlevi Marwanto Siti Shofiah