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All Journal Jurnal Teknologi Perikanan dan Kelautan Jurnal Ilmu dan Teknologi Kelautan Tropis
Solikin, Steven
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Education Library & Information Science

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PENGUKURAN NILAI TARGET STRENGTH ANAKAN IKAN GABUS (Channa striata) KONDISI TERKONTROL DENGAN SIMRAD EK-15 ECHOSOUNDER Siahaan, Gracia Tiffany; Pujiyati, Sri; Hestirianoto, Totok; Solikin, Steven
Jurnal Teknologi Perikanan dan Kelautan Vol 15 No 3 (2024): OKTOBER 2024
Publisher : Fakultas Perikanan dan Ilmu Kelautan, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24319/jtpk.15.235-244

Abstract

Ikan gabus (Channa striata) merupakan ikan karnivora yang banyak ditangkap karena rasa dagingnya yang gurih. Keberadaan ikan gabus di Indonesia menyebar dengan sangat luas, namun ancaman terhadap anakan gabus secara terus menerus dapat berakhir dengan kepunahan. Hal ini menjadi penting untuk menganalisis hubungan antara Target Strength (TS) dengan panjang total anakan ikan Gabus (Channa striata), dengan menggunakan instrumen echosounder SIMRAD EK15 untuk manajemen sumber daya, serta mengevaluasi kesehatan populasi ikan gabus. Penelitian dilakukan menggunakan 15 ekor ikan gabus dengan rentang panjang badan ikan 10-15 cm, memiliki rentang nilai TS rata-rata -46,80 dB sampai -45,01 dB. Hubungan antara panjang total ikan gabus dan nilai target strength mempunyai hubungan yang kuat dengan nilai koefisien korelasi sebesar 0,8531 dan koefisien determinasi (R2) sebesar 0,7279, dengan persamaan TS= 64,439ln(L) -215,66.
Echogram Image Analysis for Pelagic Fish School Classification in Bungus Waters, West Sumatra Lansky, Jovian; Solikin, Steven; Pujiyati, Sri; Hestirianoto, Totok
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 17 No. 3 (2025): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.17.3.69567

Abstract

Tropical marine fisheries are characterized by high species diversity but low individual abundance per species within the same water column, making the detection of distinct fish schools challenging using hydroacoustic technology. However, a hydroacoustic survey conducted in Bungus, West Sumatra, in October 2023 revealed the presence of approximately 14 identifiable fish schools of varying sizes, indicating the potential for further analytical investigation. This study aims to characterize and classify fish schools by analyzing echogram imagery and extracting key acoustic parameters through a statistical multivariate approach. Acoustic data were collected using a Simrad EK-15 echosounder operating at 200 kHz. Subsequent data processing was performed in Echoview, followed by multivariate analysis. From an initial dataset of 24 detected schools, 12 parameters were retained for analysis. These parameters were categorized into three groups: (1) energetic parameters, including target strength (TS), volume backscattering strength (Sv), area backscattering strength (Sa), skewness, and kurtosis; (2) morphometric parameters, consisting of school height, length, perimeter, and volume; and (3) bathymetric parameters, represented by average school depth. Latitude and longitude were included as supplementary spatial descriptors. Among the 12 parameters, latitude did not contribute to school characterization and was therefore excluded from further analysis. Multivariate results indicated that morphometric parameters (particularly school height and area) and energetic parameters (Sa and TS) were the most influential in differentiating school structure. Cluster analysis based on the remaining 11 parameters identified two distinct groups of fish schools: Group 1, comprising 14 schools (58.3%), and Group 2, comprising 10 schools (41.7%). These findings demonstrate that integrating hydroacoustic metrics with multivariate statistical methods provides an effective framework for identifying and characterizing fish schools in tropical waters with inherently complex species assemblages.
Co-Authors Hestirianoto, Totok Lansky, Jovian Siahaan, Gracia Tiffany Sri Pujiyati