Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of System and Computer Engineering Jurnal Ilmiah Teknik Informatika dan Komunikasi
Eva Kurniawaty
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Penerapan Teknologi Optical Characater Recognition pada Aplikasi Pemindaian Nutrisi di Label Kemasan Makanan Muhammad Suhairi; Elvi Rahmi; Eva Kurniawaty
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 1 (2025): Maret-Juni : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i1.1205

Abstract

Many consumers rarely read nutrition labels on food packaging due to difficulty understanding the information. This research develops a mobile application that can scan nutrition labels using OCR (Optical Character Recognition) technology to address this issue. The application was developed using Flutter and RAD methodology. Its main features include nutrition label scanning, health monitoring, and alerts when consumed nutrients exceed recommended limits based on age and health conditions. From testing results, OCR accuracy reached 74% for text detection and 72% for nutritional value identification. The highest accuracy of 90% was achieved under bright lighting conditions with flat labels. However, accuracy dropped dramatically to 47% with curved labels or poor lighting. Other features such as login, user profile, health monitoring, and scanning history all function properly. The developed application is expected to help consumers more easily read and understand nutritional information on food labels. Future development requires enhanced OCR algorithms to detect text more accurately under various lighting conditions and image capture angles.
Vehicle Counting System on Ro-Ro Ferry Bengkalis Route Using YOLO26n and ByteTrack Algorithm Muhamad Nasir; Tengku Musri; Eva Kurniawaty; Thomas Brian; Muhammad Rizki Kurniawan
Journal of System and Computer Engineering Vol 7 No 4 (2026): JSCE: October 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i4.2848

Abstract

Vehicle transportation management on Ro-Ro ferries requires accurate information regarding the number and type of vehicles transported during the loading process. Conventional counting methods are still performed manually, which requires time and has the potential to produce human errors. This research proposes an automatic vehicle counting system on the Bengkalis Ro-Ro ferry route using YOLO26n object detection combined with ByteTrack multi-object tracking. The system processes video recordings from Ro-Ro ferry activities and detects three vehicle categories, namely cars, motorcycles, and trucks. The detection process uses YOLO26n with a confidence threshold of 0.25 and an image size of 640 pixels. ByteTrack is implemented to maintain vehicle identities and prevent duplicate counting when vehicles cross the counting line. The experimental results show that the proposed system successfully detected and counted 20 vehicles consisting of 5 cars, 10 motorcycles, and 5 trucks. The system achieved an average processing speed of 15 frames per second, indicating that the proposed approach can support real-time vehicle monitoring on Ro-Ro ferry transportation systems.
Co-Authors Elvi Rahmi Muhamad Nasir Muhammad Rizki Kurniawan Muhammad Suhairi Tengku Musri Thomas Brian