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All Journal TEPIAN Journal of Computing and Informatics Research
Sari, Intan Meutia
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Computer Science & IT

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Implementation of Deep Learning Algorithm for Vehicle Count Monitoring System Septian, M Ridwan Dwi; Masitoh, Agustine Hana; Sari, Intan Meutia
TEPIAN Vol. 5 No. 4 (2024): December 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i4.3213

Abstract

Vehicle detection plays a crucial role in various applications such as traffic surveillance, license plate recognition, and the development of autonomous vehicles. The You Only Look Once (YOLO) object detection method is renowned for its high-speed real-time object detection capabilities. In this study, YOLO is employed to detect vehicles in images and videos. YOLO treats object detection as a direct regression problem for bounding boxes and class predictions. The aim of this research is to develop a vehicle counting system using the YOLO method. The Midpoint algorithm is utilized to calculate the midpoint between two points in a coordinate plane. Another objective is to analyze the strengths and weaknesses of the method and algorithm in the context of vehicle detection while identifying related research trends. The test results indicate that the system is capable of detecting vehicles with an average accuracy of 92.42% across four different time periods. In the morning, the system detected 156 vehicles (manual count: 147, accuracy: 94.23%); at midday, it detected 246 vehicles (manual count: 225, accuracy: 91.46%); in the evening, 377 vehicles were detected (manual count: 351, accuracy: 93.10%); and at night, the system identified 526 vehicles (manual count: 225, accuracy: 92.58%). This study contributes to the development of a more effective vehicle counting system for smart city applications while also paving the way for further research on vehicle detection under varying lighting and environmental conditions.
Sistem Pendukung Keputusan Dalam Pemilihan Siswa-Siswi Berprestasi Menerapkan Metode SAW (Simple Additive Weighting) Sari, Intan Meutia; Thyas, Lira Arum Kusumaning
Journal of Computing and Informatics Research Vol 5 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i1.2319

Abstract

In facing the development of the era and the era of technology that is developing rapidly at all times, the development of human resources is a top priority in national development, the position and position of students, have a very important role in the teaching and learning activities of students in order to improve student learning achievements in academic and non-academic fields, one of the things that motivates students to always develop themselves is to give an award as an outstanding student with the criteria that have been determined by the school. Temporary observations at Mustafa Private Vocational School in determining outstanding students are carried out manually. This method is considered still less effective and efficient. Based on this, a model for determining outstanding students is needed at Mustafa Private Vocational School with a more efficient and effective system. This system is designed using a decision support system through the Simple Additive Weighting (SAW) method. This system can display the ranking results of outstanding students based on the results of the SAW method calculations.
Co-Authors Masitoh, Agustine Hana Septian, M Ridwan Dwi Thyas, Lira Arum Kusumaning