KLASIFIKASI TIGA GENUS IKAN KARANG MENGGUNAKAN CONVOLUTION NEURAL NETWORK

Ishak Ariawan; Willdan Aprizal  Arifin; Ayang Armelita  Rosalia; Lukman; Nabila  Tufailah

doi:10.29244/jitkt.v14i2.33633

Ishak Ariawan Program Studi Sistem Informasi Kelautan, Kampus Daerah Serang, Universitas Pendidikan Indonesia, Kota Serang, 42116, Indonesia
Willdan Aprizal Arifin Program Studi Sistem Informasi Kelautan, Kampus Daerah Serang, Universitas Pendidikan Indonesia, Kota Serang, 42116, Indonesia
Ayang Armelita Rosalia Program Studi Sistem Informasi Kelautan, Kampus Daerah Serang, Universitas Pendidikan Indonesia, Kota Serang, 42116, Indonesia
Lukman Program Studi Sistem Informasi Kelautan, Kampus Daerah Serang, Universitas Pendidikan Indonesia, Kota Serang, 42116, Indonesia
Nabila Tufailah Program Studi Sistem Informasi Kelautan, Kampus Daerah Serang, Universitas Pendidikan Indonesia, Kota Serang, 42116, Indonesia

DOI: https://doi.org/10.29244/jitkt.v14i2.33633

Keywords: Convolutional Neural Network, coral fish, identification

Abstract

Reef fish are one of the essential organisms in studying coral reef ecosystems, and it is necessary to carry out an identification process to understand the pattern, structure and distribution of reef fish diversity. In addition, reef fish have a vast number and are almost similar to each other. Therefore, to speed up the process of fish identification can be done computerized. One of the automated techniques that can be done is digital image processing. This study aims to classify the image of the genus Fish (Epinephelus spp., Halichoeres spp., and Lutjanus spp.) as economically significant. Image data was obtained from the site https://www.kaggle.com/. The image classification method used is Convolutional Neural Network (CNN) which consists of two stages. The first stage is training with the backpropagation method, and the second stage is image classification using feedforward—the results of the combination of the two methods obtained an accuracy of 85,31%. In addition, the model built is quite good because the average value between precision and sensitivity is not too significant; precision is 89,92%, and sensitivity is 86,49%. Based on the analysis and evaluation that has been done, it can be concluded that the CNN classification method can be appropriately used in classifying fish images by genus.

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CLASSIFICATION OF THREE GENERA OF CORAL FISH USING CONVOLUTIONAL NEURAL NETWORK

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