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All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Rafif, Raid
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Design and Implementation of a Web- Based Vessel Daily Report Information System for Optimizing Operational Efficiency and Accounting in the Shipping Industry Rama Putra, Gustian; Fajar Ilmiyono, Agung; Rafif, Raid; Munggaran Akhmad, Dinar
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.02

Abstract

The rapid development of information technology has provided solutions to various operational challenges in the shipping industry, particularly in daily reporting systems that are still performed manually using spreadsheets. PT. LSM currently faces inefficiencies, data inaccuracy, and lack of real-time access in reporting vessel daily activities. To address these issues, this study aims to design and implement a Web-Based Vessel Daily Report Information System using PHP (Laravel Framework) and MySQL as the database. The research adopts the System Development Life Cycle (SDLC) methodology, which consists of planning, analysis, design, implementation, and testing phases. Data were collected through observation, interviews with company staff, and literature study. The system was designed with features such as vessel daily report management, vessel data management, inventory management, user management and automated report generation in PDF/CSV formats. The testing results, which include structural, functional and validation tests, show that the system operates in accordance with its design and successfully resolves the problems of the previous manual reporting process. The system enables real-time, accurate, and secure reporting that supports both operational monitoring and accounting-related decision-making. In conclusion, the developed system significantly improves the efficiency, accuracy and effectiveness of vessel daily reporting processes at PT. LSM. For future development, the system can be enhanced with additional features such as online crew attendance and ship requisition modules to further strengthen operational and accounting integration.
Advancing Smart City Infrastructure: A Deep Learning-Based Framework for Real-Time Traffic Monitoring and Violation Detection Using YOLOv11 Rama Putra, Gustian; Jaleco Forca, Adrian; Jun Gepayo Alminaza, Reiner; Delli Wihartiko, Fajar; Rafif, Raid
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1354

Abstract

Urban traffic congestion and violations of dedicated bus lanes in metropolitan cities, such as Jakarta, are significant challenges affecting the efficiency of public transportation systems. Traditional traffic monitoring methods are insufficient to address these issues, particularly in real-time violation detection. This research proposes an AI-based smart traffic monitoring framework using YOLOv11 for real-time detection of vehicle violations in TransJakarta’s Bus Rapid Transit (BRT) lanes. The study aims to improve urban mobility by enhancing the detection accuracy and speed of traffic monitoring systems. The methodology involves data collection from surveillance cameras, data annotation using Roboflow, and model training with YOLOv11, utilizing transfer learning and hyperparameter optimization. The system's performance is evaluated through precision, recall, F1-score, and mean Average Precision (mAP@0.5), as well as real-time inference speed. The results show that YOLOv11 achieves a mAP@0.5 of 0.946 and an F1-score of 0.898, demonstrating the model's high accuracy in detecting vehicle violations across different lighting conditions. Real-time inference is achieved at a rate of 35-40 FPS, making it suitable for deployment in real-world urban environments. This research concludes that the YOLOv11-based framework is an effective solution for automated traffic monitoring, offering significant implications for smart city development and intelligent transportation systems. Further research is needed to address lighting challenges and improve the system's scalability across various urban settings.
Co-Authors Delli Wihartiko, Fajar Fajar Ilmiyono, Agung Jaleco Forca, Adrian Jun Gepayo Alminaza, Reiner Munggaran Akhmad, Dinar Rama Putra, Gustian