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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) INFORMAL: Informatics Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of New Media Technology Jurnal ULTIMATICS Journal of Applied Data Sciences Prioritas : Jurnal Pengabdian Kepada Masyarakat
Maria Irmina Prasetiyowati
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Monte Carlo Algorithm Applications in Shrimp Farming: Monitoring Systems and Feed Optimization Kurniawan, Vincentius; Herkristito, Atanasius Raditya; Prasetiyowati, Maria Irmina
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i1.4301

Abstract

Indonesia is one of the world’s leading maritime nations, ranking second in fishery export value in 2020 [1]. Shrimp stands out as the most lucrative commodity, with an export value of USD 1,997.49 million [2]. Feeding shrimp plays a vital role in their growth and cultivation; however, overfeeding can result in feed residue that negatively impacts the quality of pond water and represents the biggest operational after capital expenditure [3]. The profitability of shrimp farming heavily depends on the feeding cost. This study using the Monte Carlo algorithm to track feed in shrimp and provides an optimal feeding plan. The algorithm can be used to provide feed recommendations for shrimp start from 33 days of cultivation (DoC), with an best range around 85kg to 92kg. The findings show the potential of the Monte Carlo algorithm in enhancing feeding plan in shrimp farming industries. Index Terms— Cultivation; Feeding; Feed Recommendations; Margin; Monte Carlo; Operational Cost; Pond Water; Shrimp Farming
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Windu Gata; Muhammad Haris; Maria Irmina Prasetiyowati; Sony Harianto
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1097

Abstract

Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Windu Gata; Muhammad Haris; Maria Irmina Prasetiyowati; Sony Harianto
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1097

Abstract

Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
Co-Authors Adhi Kusnadi Albertus Radityo Hadi Arnon Makarios Arya Wicaksana Benny Wijaya Dennis Gunawan, Dennis Dika Putri Metalica Dwiza Riana Fenina Twince Tobing Gata, Windu Herkristito, Atanasius Raditya Istiono, Wirawan Kurniawan, Vincentius Muhammad Haris Siswahyudianto Sony Harianto Teddy Hartanto Vina Sagita Waworuntu, Alexander Windu Gata Yaman Khaeruzzaman