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All Journal JOIV : International Journal on Informatics Visualization Journal of Economics, Business, Management, Accounting And Social Sciences
Wijaya, Candra Kusuma
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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PENGARUH FINANCIAL LITERACY, FINANCIAL ATTITUDE, FINANCIAL SELF-EFFICACY, FINANCIAL TECHNOLOGY, LOCUS OF CONTROL, LIFESTYLE TERHADAP FINANCIAL MANAGEMENT BEHAVIOUR PADA MAHASISWA SURABAYA HOBBY MODIF MOBIL Wijaya, Candra Kusuma
JOURNAL OF ECONOMICS, BUSINESS, MANAGEMENT, ACCOUNTING AND SOCIAL SCIENCES Vol. 2 No. 2 (2024): JANUARY 2024
Publisher : PUTRA JAWA PUBLISHER

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Abstract

This study aims to investigate the influence of financial literacy, financial attitude, financial self-efficacy, financial technology, locus of control, and lifestyle on the financial management behavior of Surabaya students who have a hobby of modifying cars. The sample for this study consisted of 168 Surabaya students interested in car modifications during the period from 2020 to 2022. The analysis technique employed in this study was multiple linear regression using SPSS version 26. The results of the study indicate that financial literacy has a significant effect on financial management behavior, financial attitude also has a significant effect on financial management behavior, financial self-efficacy does not have a significant effect on financial management behavior, financial technology has a significant influence on financial management behavior, locus of control has a significant influence on financial management behavior, and lifestyle does not have a significant effect on financial management behavior.
Real-Time Embedded Vision System for Road Damage Detection Utilizing Deep Learning Putri, Ambarwati Rizkia; Irwansyah, Arif; Arifin, Firman; Purwantini, Elly; Wijaya, Candra Kusuma
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3661

Abstract

Accidents resulting from road damage are becoming a serious concern, emphasizing the need for efficient monitoring systems and timely government intervention. This research highlights the potential of advanced AI-driven solutions in road safety management, providing a practical approach to efficiently monitoring and maintaining road conditions. It presents a real-time embedded vision system for automatic road damage detection using deep learning techniques. The system is designed to classify six types of road damage and has been implemented on two platforms: Jetson Nano and a personal computer or laptop. A comparative analysis was conducted to evaluate accuracy, computational performance, and power efficiency. The study employs YOLO (v5, v7, v8) and EfficientDet algorithms for detecting road damage. Experimental results indicate that EfficientDet achieves the highest accuracy at 88%, while YOLO attains 63%. In terms of computational performance, YOLOv8 delivers the highest frame rate, reaching 25 FPS on the Jetson Nano. Power efficiency analysis reveals that YOLOv8 on the Jetson Nano is six times more energy-efficient compared to its implementation on a laptop. Likewise, EfficientDet on Jetson Nano demonstrates three times better energy efficiency than on a laptop. These findings underscore the feasibility of deploying AI-powered embedded vision systems for detecting road damage. The use of deep learning models on energy-efficient platforms, such as Jetson Nano, enhances real-time performance while minimizing power consumption. Future research should focus on optimizing these models to enhance performance on edge devices while further assessing their practical applications in real-world environments.
Co-Authors Elly Purwanti Firman Arifin, Firman Irwansyah, Arif Putri, Ambarwati Rizkia