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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Nisa, Suci Lutfia
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Computer Science & IT Decision Sciences, Operations Research & Management

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Vehicle Class Prediction at Toll Gate Using Deep Learning Nisa, Suci Lutfia; Soim, Sopian; Agung, Muhammad Zakuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9833

Abstract

In the era of digitalization and automation, efficiency in the traffic management system at toll gates is very important. One of the efforts to improve this efficiency is to develop an automatic vehicle class detection system using deep learning technology, especially Convolutional Neural Network (CNN). This research aims to design and implement a CNN model that can identify and classify the types of vehicles passing through toll gates. The model development process includes collecting and annotating vehicle image data, data pre-processing, and CNN model training and testing. The evaluation results show that the developed model can achieve an accuracy of about 96% in detecting vehicle classes, so it can be integrated with the toll gate system to increase the speed and accuracy in the vehicle classification process. Thus, this solution is expected to reduce the waiting time of toll users and improve operational efficiency.
Co-Authors Agung, Muhammad Zakuan Sopian Soim, Sopian