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Identification of the Snail Oncomelania hupensis Lindoensis as Schistotomiasi Host Using CNN

Advance Sustainable Science, Engineering and Technology (ASSET) · Vol. 5 No. 3 (2023)

DOI
10.26877/asset.v5i3.17195
ISSN
2715-4211, 2715-4211
Terbit
2023-10-31
Penerbit
Universitas PGRI Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Schistosomiasis is declared by the World Health Organization as a neglected tropical disease. In Indonesia, schistosomiasis is endemic in three regions of Central Sulawesi. In 2022, the schistosomiasis prevalence rate in humans was 1.44%, far from the government's target of 0% prevalence in humans, snails, and mammals by 2025. The role of technology is to identify O.hupensis Lindoensis snails as schistosomiasis hosts among snails in schistosomiasis endemic areas. This system can make it easier for people to recognize O.hupensis Lindoensis snails and can speed up the identification process and reduce survey costs for officers. The identification system is made with digital image processing techniques using the CNN algorithm with Mobile Net architecture. Model updating in the form of 4 classes with 1200 image data. The results of training accuracy of 93% and validation accuracy of 87% were obtained. The training loss function is 0.17, and the validation loss is 0.33

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  • Tadulako University
  • National Research and Innovation Agency
  • North Sumatra Islamic University
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Rekan penulisOrganisasiArtikel bersama
Rahmah LailaTadulako University4
Anita Ahmad KasimTadulako University4
Rizka ArdiansyahTadulako University3
Mohammad Yazdi PusadanTadulako University3
Muhammad Fajar RifaiTadulako University2
Ryfial AzharTadulako University2
Jonathan Albert UswaryTadulako University2
Muhammad JindanTadulako University2
Syahrullah SyahrullahTadulako University2
Junaidi JunaidiNorth Sumatra Islamic University1
Iman SetiawanTadulako University1
Fadjryani FadjryaniTadulako University1
Mohammad BataraTadulako University1
Nurmasita IsmailAfiliasi tidak diketahui1
Alvin Christian Davidson TherryTadulako University1
Ryan Adi PutraTadulako University1
Roy Glen Albert MassieNational Research and Innovation Agency1
Meiske Elisabeth KoraagNational Research and Innovation Agency1
Samarang SamarangNational Research and Innovation Agency1
Made Agus NurjanaNational Research and Innovation Agency1
Octaviani OctavianiNational Research and Innovation Agency1
Dwi Shinta AngreniTadulako University1
Clivent Gerhard SondakhTadulako University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.upgris.ac.id/index.php/asset/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Identification of the Snail Oncomelania hupensis Lindoensis as Schistotomiasi Host Using CNN</dc:title>
	<dc:creator>Alghifari, Muh Alif</dc:creator>
	<dc:creator>Ngemba, Hajra Rasmita</dc:creator>
	<dc:creator>Widjaja, Junus</dc:creator>
	<dc:creator>Hendra, Syaiful</dc:creator>
	<dc:creator>Yazdi, Muhammad</dc:creator>
	<dc:creator>Joefrie, Yuri Yudhaswana</dc:creator>
	<dc:subject xml:lang="en-US">World Health Organization, Schistosomiasis, Endemic, O.hupensis lindoensis, CNN</dc:subject>
	<dc:description xml:lang="en-US">Schistosomiasis is declared by the World Health Organization as a neglected tropical disease. In Indonesia, schistosomiasis is endemic in three regions of Central Sulawesi. In 2022, the schistosomiasis prevalence rate in humans was 1.44%, far from the government's target of 0% prevalence in humans, snails, and mammals by 2025. The role of technology is to identify O.hupensis Lindoensis snails as schistosomiasis hosts among snails in schistosomiasis endemic areas. This system can make it easier for people to recognize O.hupensis Lindoensis snails and can speed up the identification process and reduce survey costs for officers. The identification system is made with digital image processing techniques using the CNN algorithm with Mobile Net architecture. Model updating in the form of 4 classes with 1200 image data. The results of training accuracy of 93% and validation accuracy of 87% were obtained. The training loss function is 0.17, and the validation loss is 0.33</dc:description>
	<dc:publisher xml:lang="en-US">Universitas PGRI Semarang</dc:publisher>
	<dc:contributor xml:lang="en-US">Magister Pendidikan Fisika, FKIP Universitas Sriwijaya</dc:contributor>
	<dc:date>2023-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.upgris.ac.id/index.php/asset/article/view/17195</dc:identifier>
	<dc:identifier>10.26877/asset.v5i3.17195</dc:identifier>
	<dc:source xml:lang="en-US">Advance Sustainable Science, Engineering and Technology; Vol 5, No 3 (2023): August-October; 02303012</dc:source>
	<dc:source>2715-4211</dc:source>
	<dc:source>2715-4211</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.upgris.ac.id/index.php/asset/article/view/17195/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Advance Sustainable Science Engineering and Technology</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>