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All Journal Jurnal SPEKTRUM PROSIDING CSGTEIS 2013 JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Ilmiah Ilmu Sosial Scientific Journal of Informatics
Fajar Purnama
Jurusan Teknik Elektro, Fakultas Teknik, Universitas Udayana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice

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Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network Latumakulita, Luther Alexander; Arya Astawa, I Nyoman Gede; Mairi, Vitrail Gloria; Purnama, Fajar; Wibawa, Aji Prasetya; Jabari, Nida; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1082

Abstract

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.
Co-Authors Aditya Widhiatama, Ngakan Putu Aji Prasetya Wibawa Alviana Ratna Malinda Amanda Saraswati, Ni Putu Andika Pranata, I Kadek Anisa Dalilah Dalilah, Anisa Duman Care Khrisne Dwika Prihambodo, Prakoso Emmy Rosiana Dewi , Ni Wayan Gede Manuaba, Ida Bagus Gede Sukadarmika I Gusti Agung Putu Raka Agung, I Gusti Agung Putu I Made Arsa Suyadnya I Made Oka Widyantara I Nyoman Gede Arya Astawa I Wayan Shandyasa Islam, Noorul Jabari, Nida Juliawan Pawana, I Wayan Adi Lingga Pratyaksa Nugraha, Anak Agung Lintin, Yosep Tara Luther Latumakulita Mairi, Vitrail Gloria Maisha Putra, I Gusti Agung Ngurah Malinda, Alviana Ratna Monicha, Windha Ngurah Indra ER Nur Adl, Waliyin Nyoman Putra Sastra Oktapiyansyah, Recky Pangidoan, Annes Maria Paramitha Sekar Putri A.P., Ni Made Putu Wira Buana Recky Oktapiyansyah Saputra, Deva Dimastawan Windha Monicha