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Wijopriono Wijopriono
Research Centre for Fisheries Management and Conservation, Jakarta
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology

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ACOUSTIC CLASSIFICATION OF FRESHWATER FISH SPECIES USING ARTIFICIAL NEURAL NETWORK: EVALUATION OF THE MODEL PERFORMANCE Zulkarnaen Fahmi; Wijopriono Wijopriono
Indonesian Fisheries Research Journal Vol 19, No 1 (2013): (June 2013)
Publisher : Research Center for Fisheries

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2647.724 KB) | DOI: 10.15578/ifrj.19.1.2013.19-24

Abstract

Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. Nonetheless, such techniques are limited by problems of species identification. Several methods and techniques have been used in addressing the problem of acoustic identification species and one of them is Artificial Neural Networks (ANNs). In this paper, Back propagation (BP) and Multi Layer Perceptron (MLP) of the Artificial Neural Network were used to classify carp (Cyprinus carpio), tilapia (Oreochromis niloticus), and catfish (Pangasius hypothalmus). Classification was done using a set of descriptors extracted from the acoustic data records, i.e. Volume Back scattering (Sv), Target Strength (TS), Area Back scattering Strength, Skewness, Kurtosis, Depth, Height and Relative altitude. The results showed that the Multi Layer Perceptron approach performed better than the Back propagation. The classification rates was 85.7% with the multi layer perceptron (MLP) compared to 84.8% with back propagation (BP) ANN.
Co-Authors Zulkarnaen Fahmi