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A Novel Fishing Finder Technique Based on VMS Data in Indonesia

Published: 21 January 2020 Publication History

Abstract

Indonesian waters are very wide and have very high fisheries potential. This clearly benefits the Indonesian fishing industry. Tuna is a primary trading commodity in this industry, but catching this fish is not easy. In addition, the movement of tuna follows the pattern of changes in Earth's temperature, so determining its position clearly requires adequate equipment. The original purpose of Vessel Monitoring System (VMS) is for enforcement and control of vessel sailing. In this paper, we use VMS to find the position of tuna to reduce fishing costs for Indonesia tuna fishermen such as fuel and labor costs. This study does three things. Firstly, conduct tracking modeling of the movements of each type of fishing vessel in Indonesian waters, and use cross-reference with weather data and earth conditions, then filtering the data for fishing tuna vessel only. Secondly, develop visualization techniques resulting from algorithms by programming Python Dash. Finally, develop fish position prediction techniques using the Machine Learning ensemble by Support Vector Machine technology. It concludes that system can predict the location of tuna correctly 97.6%.

References

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Ortiz, M. et al. 2012. Preliminary Analyses of the Iccat Vms Data 2010-2011 To Identify Fishing Trip Behavior and Estimate Fishing Effort. Sci. Pap. ICCAT. 125, 691 (2012), 462--481.
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Pornsupikul, S. et al. 2017. Trajectory mining from VMS data for identifying fishing tackles. (2017), 35--40.
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Nilsson, P. and Ziegler, F. 2008. Spatial distribution of fishing effort in relation to seafloor habitats in the Kattegat, a GIS analysis. Aquatic Conservation: Marine and Freshwater Ecosystems. 18, August 2006 (2008), 432--445.
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Wang, Y. et al. 2014. Analyses of trawling track and fishing activity based on the data of vessel monitoring system (VMS): A case study of the single otter trawl vessels in the Zhoushan fishing ground. Journal of Ocean University of China. 14, 1 (2014), 89--96.
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ICAAI '19: Proceedings of the 3rd International Conference on Advances in Artificial Intelligence
October 2019
253 pages
ISBN:9781450372534
DOI:10.1145/3369114
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  • Northumbria University: University of Northumbria at Newcastle

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 January 2020

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Author Tags

  1. Support Vector Machine
  2. Tuna
  3. Vessel Monitoring System

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  • Research-article
  • Research
  • Refereed limited

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  • Directorate General Of Strenghtening Research and Development of the ministry of research, Technology and higher education

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ICAAI 2019

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