cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jordy Lasmana Putra
Contact Email
jordy.jlp@nusamandiri.ac.id
Phone
+6221-231170
Journal Mail Official
jurnal.coscience@bsi.ac.id
Editorial Address
Jl. Kramat Raya No.98, RT.2/RW.9, Kwitang, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta 10450 (Gedung Rektorat Universitas Bina Sarana Informatika)
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
Computer Science (CO-SCIENCE)
Published by Universitas Bina Sarana Informatika
ISSN : -     EISSN : 27749711     DOI : https://doi.org/10.31294/coscience
Core Subject : Science,
Computer Science & IT
Computer Science (CO-SCIENCE) pertama kali publikasi tahun 2021 dengan nomor ISSN (Elektonik): 2774-9711 yang diterbitkan oleh Lembaga Ilmu Pengetahuan Indonesia (LIPI). Computer Science (CO-SCIENCE) adalah jurnal yang diterbitkan oleh Program Studi Ilmu Komputer Universitas Bina Sarana Informatika. Computer Science (CO-SCIENCE) terbit 2 kali setahun (Januari dan Juli) dalam bentuk elektronik. Redaksi menerima naskah berupa artikel ilmiah dan penelitian pada bidang: Networking, Aplication Mobile, Software Engineering, Web Programming, Mobile Computing, Cloud Computing, Data Mining, dan Aplikasi Sains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 131 Documents
 10   11   12   13   14 
Performance Evaluation of YOLOv8 for Railway Switching Operation Safety Monitoring Selendra, Aulya Anggita Putri; Arifianto, Teguh; Winjaya, Fathurrozi
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.

Page 14 of 14 | Total Record : 131

 10   11   12   13   14 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2026): January 2026 Vol. 5 No. 2 (2025): Juli 2025 Vol. 5 No. 1 (2025): Januari 2025 Vol. 4 No. 2 (2024): Juli 2024 Vol. 4 No. 1 (2024): Januari 2024 Vol. 3 No. 2 (2023): Juli 2023 Vol. 3 No. 1 (2023): Januari 2023 Vol. 2 No. 2 (2022): Juli 2022 Vol. 2 No. 1 (2022): Januari 2022 Vol. 1 No. 2 (2021): Juli 2021 Vol. 1 No. 1 (2021): Januari 2021 More Issue