Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Computer Science (CO-SCIENCE) Journal of Railway Transportation and Technology
Fathurrozi Winjaya
Indonesian Railway Polytechnic
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Study Quality of Voltage on Single Track AC Railway Traction Electrification Andri Pradipta; Santi Triwijaya; Fathurrozi Winjaya; Arief Darmawan; Agustinus Prasetyo Edi W
Journal of Railway Transportation and Technology Vol. 2 No. 1 (2023): March
Publisher : Politeknik Perkeretaapian Indonesia Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37367/jrtt.v2i1.21

Abstract

Voltage fluctuation is one of important parameter for power quality in electrification parameter. The voltage parameter is said to be good if the voltage level does not exceed or less than the standard voltage. In the operation of electric trains, several conditions can cause over and under voltage. Like when a train accelerates or decelerates it will affect the system voltage. This research study about voltage fluctuation on AC railway electrification. The method used in this study is to simulate a model of railway electrification such as traction substations, overhead electrification and rail infrastructure. System modeling is done using open power net software. The results of this simulation show the condition of voltage fluctuations on the bus, overhead wire and pantograph sides
Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring Aulya Anggita Putri Selendra; Teguh Arifianto; Fathurrozi Winjaya
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.11674

Abstract

Safety in railway shunting operations requires continuous monitoring of train distance and speed to reduce the risk of operational accidents. In practice, shunting activities are still highly dependent on manual observation and verbal communication, while the performance of vision based safety systems under real operational conditions remains uncertain. In addition, comprehensive performance evaluations of deep learning based object detection models in real shunting environments, particularly under different hardware capabilities and lighting conditions, are still limited. This study aims to evaluate the performance of the YOLOv8 algorithm for real-time distance and speed monitoring during railway shunting operations. The system was tested using a camera-based detection approach under different processor configurations, namely an internal CPU and an RTX GPU, and under morning, daytime, and nighttime lighting conditions. System performance was evaluated based on accuracy, precision, and real-time detection capability across these conditions. The results show that the system achieved an average accuracy of 87.32% when operating on a CPU which increased to 91.30% when using a GPU. Optimal performance was observed under adequate daylight conditions, while reduced lighting led to a decline in performance, particularly on CPU-based processing. These findings indicate that hardware configuration and lighting conditions play a critical role in determining the reliability of YOLOv8-based safety monitoring systems for railway shunting operations.
Co-Authors Agustinus Prasetyo Edi W Andri Pradipta Arief Darmawan Aulya Anggita Putri Selendra Santi Triwijaya Teguh Arifianto