Article Per Year (5 Year)
p-Index From 2021 - 2026
1.683
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Infotekmesin Journal of Energy and Electrical Engineering (JEEE) Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi dan Manajemen Industri Terapan Information Technology Education Journal Jurnal Polimesin Jurnal Keselamatan Transportasi Jalan Journal of Renewable Energy and Smart Device Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Helmi Wibowo
Politeknik Keselamatan Transportasi Jalan
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Studi Eksperimen Penggunaan Diesel Particulat Filter Terhadap Temperatur Oli Mesin, Air Radiator, Dan Exhaust Manifold Mesin Diesel Moch. Aziz Kurniawan; Helmi Wibowo; Aat Eska Fahmadi; Nasrul Amin; Muhammad Farras
Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Vol. 12 No. 2 (2025): JURNAL KESELAMATAN TRANSPORTASI JALAN (INDONESIAN JOURNAL OF ROAD SAFETY)
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46447/ktj.v12i2.771

Abstract

The use of vehicles using diesel engines is increasing and has a direct impact on increasing exhaust emissions, especially particulate matter (PM), which is harmful to health and the environment. This study aims to analyze the installation of a honeycomb Diesel Particulate Filter (DPF) made of galvalum on exhaust emissions and engine temperature in a Mitsubishi L300 vehicle. The DPF was designed using galvalum material with a square honeycomb configuration and glasswool variations of 50, 100, and 150 grams. The test was conducted experimentally by comparing conditions without DPF and after DPF installation, including testing exhaust emissions, engine oil temperature, radiator water temperature, and exhaust manifold temperature. The installation of a diesel particulate filter (DPF) can reduce exhaust emissions by up to 37.1% at DPF 150 variations. DPF installation also relatively increases the temperature of radiator water, engine oil, and exhaust manifold in diesel engines. The largest temperature increase in exhaust manifold temperature is up to 8.02% compared to without using DPF at idle conditions. This temperature increase is caused by the honeycomb and glasswool structures that can create obstacles to the flow of exhaust gases. When the engine speed reaches 2000 rpm, there is an increase in exhaust manifold temperature of up to 15.09% compared to idle speed. This increase is due to faster engine speed so that combustion heat also increases.
Object Detection of Motor Vehicle Suspension Systems Based on the YOLOv8 Algorithm: Supporting Sustainable Industry and Innovation (SDG 9) Helmi Wibowo; Nurul Muzakki Rihhadatul ‘Aisy; Muhammad Iman Nur Hakim; Mokhammad Rifqi Tsani; Setya Wijayanta
Journal of Current Studies in SDGs Vol. 3 No. 2 (2027): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.3.2.192

Abstract

Objective: To develop an object detection system for identifying motor vehicle suspension system components using the YOLOv8 algorithm. Specifically, this study focused on improving the efficiency and accuracy of undercarriage inspection processes, which are commonly conducted manually and require technical knowledge to recognize suspension components and detect potential damage. This research contributes to automotive inspection innovation and supports the development of sustainable industrial technology in accordance with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Method: The study employed a computer vision-based approach using the YOLOv8 object detection algorithm for recognizing suspension system components in motor vehicles. The dataset consisted of 1000 suspension system images collected from mandatory vehicle inspection activities at motor vehicle testing facilities. Results: The results showed that the YOLOv8-based detection model could identify suspension system components with an accuracy of up to 95%. Furthermore, the trained model successfully detected oil leakage damage on shock absorber components with an accuracy of 92%. The evaluation results indicate that the proposed system can effectively recognize suspension components under different inspection conditions and provide reliable assistance for vehicle undercarriage inspection processes. Novelty: The study provides a novel implementation of the YOLOv8 deep learning algorithm for automated suspension system inspection in motor vehicles by integrating computer vision technology into the vehicle testing process. The developed system contributes to automotive technology innovation and supports the advancement of smart inspection infrastructure in line with SDG 9 (Industry, Innovation, and Infrastructure).
Driver drowsiness detection using YOLO based deep learning models Helmi Wibowo; Muh Irhas Rafiqi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10680

Abstract

The incidence of traffic accidents in Indonesia has been escalating, predominantly attributed to human factors such as fatigue and drowsiness. This study presents the implementation of a deep learning-based drowsiness detection system utilizing the you only look once (YOLO) architecture to enhance vehicular safety. Three YOLO model variants (YOLOv5, YOLOv8, and YOLOv10) were evaluated using a dataset comprising 1,000 annotated images across four classes: alert, low vigilance, drowsy, and microsleep. A quantitative experimental methodology was employed, with performance assessed through precision, recall, accuracy, and F1-score metrics. Experimental results demonstrate that YOLOv8 (medium and small variants) achieved superior overall performance, exhibiting a balanced optimization across all evaluation metrics. YOLOv5 yielded the highest recall, suggesting its suitability for comprehensive detection tasks, whereas YOLOv10 demonstrated enhanced computational efficiency without significant performance degradation. Based on these findings, YOLOv8 is recommended as the most effective model for real-world deployment, while YOLOv5 and YOLOv10 offer viable alternatives depending on specific operational requirements. This study contributes to the advancement of early warning systems for driver drowsiness detection, with the broader aim of mitigating traffic accident risks.
Co-Authors Aat Eska Fahmadi Agung Tri Febrianto Andhika Putra Perdana Asep Ridwan Dhi Astuti Dodik Wahyu Wiratama Faza Asfarin Ajrun Adhim Hanif Adhi Yudhitami I Gede Indra Perdana MOCH. AZIZ KURNIAWAN Mokhammad Rifqi Tsani Muh Irhas Rafiqi Muhammad Faris Haidar Muhammad Farras Muhammad Iman Nur Hakim Nasrul Amin Novianto, Arief Nurul Muzakki Rihhadatul ‘Aisy Rifano Riza Pahlevi Sabrina Sabrina Setya Wijayanta Srianto Srianto Sugiyarto Widiandaru, Nanang Okta