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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Junus Widjaja
National Research and Innovation Agency, Center for Public Health and Nutrition Research, Jl. M.H. Thamrin No. 8, Jakrta Pusat 10340, Jakarta, Indonesia
Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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Identification of the Snail Oncomelania hupensis Lindoensis as Schistotomiasi Host Using CNN Muh Alif Alghifari; Hajra Rasmita Ngemba; Junus Widjaja; Syaiful Hendra; Muhammad Yazdi; Yuri Yudhaswana Joefrie
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.17195

Abstract

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
Co-Authors Hajra Rasmita Ngemba Muh Alif Alghifari Muhammad Yazdi Syaiful Hendra Yuri Yudhaswana Joefrie