Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS)
Alannuari, Fiky
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Vehicle License Plate Object Detection for Vehicle Registration Using Fuzzy Logic Alannuari, Fiky; Sarimole, Frencis Matheos; Mulyana, Dadang Iskandar
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3055

Abstract

Object detection of vehicle license plates plays a role in the efficiency of vehicle data collection systems. There are many factors that make the accuracy and speed of detection on vehicle license plates less than optimal, causing errors in the detection process. The factors that affect the accuracy of object detection of vehicle license plates include clarity, lighting, shadows, color, font type, weather, and others. Based on the advantages of the Fuzzy Logic approach in handling various vague factors and uncertain data, it is hoped that this method can help the detection process to be more accurate and faster. This research aims to develop a method for detecting vehicle license plate objects using the Fuzzy Logic approach so that it can be applied in diverse environments to produce data with consistent accuracy. This research involves the development of software integrated with computers and cameras for vehicle license plate recognition, and also takes some data sources and code from libraries already available in the programming language used. The results of the tests conducted, detection using this Fuzzy Logic approach has an accuracy rate of up to 93.33% and the accuracy of reading the text stored in the database reaches 63.66%.
Co-Authors Dadang Iskandar Mulyana` Sarimole, Frencis Matheos