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All Journal Journal of Railway Transportation and Technology
Al Ghifari, Sultan Rasya Nair
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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

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Railway Detection Of Axle Detection Patterns Using The KNN Method Winjaya, Fathurrozi; Feryando, Dara Aulia; Al Ghifari, Sultan Rasya Nair; Fajri, Saian Nur
Journal of Railway Transportation and Technology Vol. 4 No. 1 (2025): March
Publisher : Politeknik Perkeretaapian Indonesia Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37367/jrtt.v4i1.47

Abstract

The track circuit is a device used to determine whether a vehicle is on the track. This device is installed on the track and needs to be monitored to ensure it functions properly. All objects detected in practice are considered railway vehicles, even though the detected objects may not be railway vehicles. Accurate and reliable detection technology is crucial for preventing accidents and keeping train traffic safe. This detection system uses metal sensors to identify objects as railway or non-railway assets. This is done based on two variables: the frequency of the object's metal and the object's speed. The test results show that this tool can classify objects according to their class. The class with no objects has an accuracy level of 100%, while the class of non-railway has an accuracy level of 86.67%. The railway class trials using the inspection train axle show an accuracy level of 93.33%.
Co-Authors Feryando, Dara Aulia Saian Nur Fajri, Saian Nur Winjaya, Fathurrozi