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IJMLC 2019 Vol.9(5): 706-711 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.862

Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm

Muhammad Ramadhani and Devi Fitrianah

Abstract—The aim of this study is to map the tuna fishing zones based on the daily fish catch data from the Hindian Ocean. With the study, it is expected to deliver a potential tuna fishing zones mapping, where it is based on the number of catch along with its spatial data. The study utilized a data mining approach with DBSCAN algorithm as the method to cluster the data. The study yields information that the Bigeye tuna is dominated the catch in the west monsoon, while Yellowfin tuna dominated the catch in the east monsoon. Based on the trial using the DBSCAN algorithm, we know that the optimal Eps and MinPts value are 1.5 and 5 respectively to generate a convergence cluster.

Index Terms—Data mining, DBSCAN algorithm, spatial analysis, clustering, rapidminer.

The authors are with Computer Science of Universitas Mercu Buana, Jakarta, Indonesia (e-mail: 41515010087@student.mercubuana.ac.id, devi.fitrianah@mercubuana.ac.id).


Cite: Muhammad Ramadhani and Devi Fitrianah, "Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 706-711, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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