Identification of Brown Seaweed on The North and South Coasts of Java Island by Machine Learning
| Main Authors: | Natasya, Putri, Suryono , Chrisna Adhi |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
CV. Hadid Mukti Karya
, 2024
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| Subjects: | |
| Online Access: |
https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16 https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16/13 |
| ctrlnum |
article-16 |
|---|---|
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en">Identification of Brown Seaweed on The North and South Coasts of Java Island by Machine Learning</title><creator lang="en">Natasya, Putri</creator><creator lang="en">Suryono , Chrisna Adhi</creator><subject lang="en">Brown Seaweed</subject><subject lang="en">Identification</subject><subject lang="en">Machine Learning</subject><description lang="en">Seaweed is one of the marine organisms that can be found in almost coastal waters of Indonesia. Brown seaweed is a group of multicellular algae that have adapted to the marine environment. This study uses morphological identification methods for brown seaweed, further facilitated by utilizing machine learning technology. The aim of this research is to compare the identification based on morphological characteristics and by machine learning. The study focused on the North Coast of Teluk Awur and the South Coast of Krakal, Java Island, as the locations for field sample collection, utilizing three stations per water area with the method of collecting images of brown seaweed. The water quality parameters were determined as supporting data of environmental condition. The results of identification with machine learning compared with manual identification gave similar results. These show that on the North Coast, the genus Sargassum was identified with a high accuracy rate of 99.11%, while on the South Coast, the genus Sargassum was identified with an accuracy rate of 99.00%, the genus Padina with an accuracy rate of 99.15%, the genus Turbinaria 98.01%, and the genus Dictyota 96.42%. The growth of brown algae in the North Coast of Teluk Awur and the South Coast of Krakal was influenced by water quality factors such as temperature, salinity, pH, dissolved oxygen, and brightness.</description><publisher lang="en">CV. Hadid Mukti Karya</publisher><date>2024-05-29</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>File:application/pdf</type><identifier>https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16</identifier><identifier>10.61741/a17bdr35</identifier><source lang="en">Journal of Marine Biotechnology and Immunology; Vol. 2 No. 2 (2024): Mei 2024; 1-6</source><source lang="id">JOURNAL OF MARINE BIOTECHNOLOGY AND IMMUNOLOGY; Vol 2 No 2 (2024): Mei 2024; 1-6</source><source>3026-1457</source><source>3026-5274</source><source>10.61741/g6h31c95</source><language>eng</language><relation>https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16/13</relation><rights lang="en">Copyright (c) 2024 Journal of Marine Biotechnology and Immunology</rights><rights lang="en">https://creativecommons.org/licenses/by-nc-sa/4.0</rights><recordID>article-16</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:Journal |
| author |
Natasya, Putri Suryono , Chrisna Adhi |
| title |
Identification of Brown Seaweed on The North and South Coasts of Java Island by Machine Learning |
| publisher |
CV. Hadid Mukti Karya |
| publishDate |
2024 |
| topic |
Brown Seaweed Identification Machine Learning |
| url |
https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16 https://ejournal.immunolmarbiotech.com/index.php/JMBI/article/view/16/13 |
| contents |
Seaweed is one of the marine organisms that can be found in almost coastal waters of Indonesia. Brown seaweed is a group of multicellular algae that have adapted to the marine environment. This study uses morphological identification methods for brown seaweed, further facilitated by utilizing machine learning technology. The aim of this research is to compare the identification based on morphological characteristics and by machine learning. The study focused on the North Coast of Teluk Awur and the South Coast of Krakal, Java Island, as the locations for field sample collection, utilizing three stations per water area with the method of collecting images of brown seaweed. The water quality parameters were determined as supporting data of environmental condition. The results of identification with machine learning compared with manual identification gave similar results. These show that on the North Coast, the genus Sargassum was identified with a high accuracy rate of 99.11%, while on the South Coast, the genus Sargassum was identified with an accuracy rate of 99.00%, the genus Padina with an accuracy rate of 99.15%, the genus Turbinaria 98.01%, and the genus Dictyota 96.42%. The growth of brown algae in the North Coast of Teluk Awur and the South Coast of Krakal was influenced by water quality factors such as temperature, salinity, pH, dissolved oxygen, and brightness. |
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CV Hadid Mukti Karya |
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Marine Science mariculture Marine biotechnoloy Marine Diseases |
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JAWA TENGAH |
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