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Temporal Forecasting System of Potential Catching Areas of Skipjack Tuna in Bone Sea Using Artificial Neural Network
Rifai S.N.
Proceedings of the International Conference on Electrical Engineering and Informatics
Abstract
Indonesia's potential in the field of capture fisheries is very supportive of the economy, one of which is large pelagic fish such as skipjack tuna. The distribution of skipjack tuna in the waters of Bone Regency, South Sulawesi, which tends to be dynamic, is very important to determine its distribution. Therefore, the purpose of this study is to forecast the potential point of distribution of skipjack tuna temporally by utilizing data mining and Artificial Neural Network algorithms. Feature extraction was used in the form of oceanographic data that affects the habitat of skipjack tuna in waters such as Sea Surface Temperature (SST) and chlorophyll-a for the last five years (2017-2021) with a total of 34,800 training data and 8,700 test data. The performance of the system was evaluated using the confusion matrix. The results showed an accuracy value of 94.89% and an F1 score of 92.09%. The application of this forecasting system is very useful for determining the skipjack fishing calendar temporally to increase the effectiveness and efficiency of costs, time, and effort.