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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TEPIAN
Masitoh, Agustine Hana
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Sistem Pendukung Keputusan Pemilihan Ekstrakulikuler Sekolah Terbaik dengan Menggunakan Metode Multi Attribute Ultility Theory (MAUT) Masitoh, Agustine Hana
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7836

Abstract

The development of technology that is currently developing very rapidly is very helpful in learning activities at school. Technology has a very important role in education. Education is one of the potentials that is needed to improve activities in schools. These activities are extracurricular activities. Extracurricular or extracurricular activities are activities to develop the talents of students who are outside the classroom or outside class hours and are not required by students to carry out these activities. school as the goal of increasing knowledge, skills, and insight as well as shaping the character of students according to their respective interests and talents. This study aims to build a decision support system (DSS). Where has a function in determining decision making in the process of determining the best school extracurricular activities by establishing the MAUT method. And get the results of the first rank correctly and the first alternative is A2 with a preference value of 0.806 which is found in volleyball exuls.
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.
Co-Authors Sari, Intan Meutia Septian, M Ridwan Dwi