— Prediction for potential fishing zone is one of the important activities concerning for the tuna fishing exploration, conservation and management. Accurate prediction will give more efficient in fishing activities. One of the way to predict is the classification techniques. Currently, as the state of the art, most of the methods utilize the chlorophyll and SST features. However, there are still other parameters that can be utilized. In this paper, the other parameters are then observed: ocean currents and salinity feature. First the results shows that, taking a part of ocean currents together with the chlorophyll and SST feature combination gives the improvement on the prediction. On other hand, this ocean currents feature is then substituted with the salinity, and the result shows that the combination between salinity, chlorophyll, and SST also increases the result. Finally, the ocean current and salinity parameters are combined together with chlorophyll and SST parameters and the result was surprising. It is found that the last feature combination which includes Chlorophyll, SST, Ocean current and salinity gives the highest result in classification (in Naïve Bayes reaches 69.03%, Decision Tree reaches 82.32% and SVM reaches 68.30% of accuracy) compared to the “baseline” feature combination including only Chlorophyll and SST (in Naïve Bayes reaches 57.44%, Decision Tree reaches 58.91% and SVM reaches 56.74% of accuracy). Therefore it is suggested that the proposed feature can be harnessed for the better prediction of potential fishing zone.
— Feature exploration, potential tuna fishing zones, classification, chlorophyll, sea surface temperature (SST), ocean currents, salinity.
The authors are with the Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia. Devi Fitrianah is also with the Faculty of Computer Science, Universitas Mercu Buana, Jakarta 11650, Indonesia (e-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite: Devi Fitrianah, Nursidik Heru Praptono, Achmad Nizar Hidayanto, and Aniati Murni Arymurthy, " Feature Exploration for Prediction of Potential Tuna Fishing Zones," International Journal of Information and Electronics Engineering vol. 5, no. 4, pp. 270-274, 2015.