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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series TEKNOLOGI: Jurnal Ilmiah Sistem Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Journal of Information Systems Engineering and Business Intelligence Jurnal Inspiration Indonesian Journal of Electrical Engineering and Computer Science Sewagati
Wijayanti Nurul Khotimah
Departemen Informatika Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : IPTEK Journal of Proceedings Series

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Aquaculture Water Quality Prediction using Smooth SVM Wijayanti Nurul Khotimah
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.306

Abstract

Aquaculture, aqua farming, is the farming of aquatic organism such as fish, crustaceans, mollusk and aquatic plan. There are many factors that influence the production of aquaculture such as food stocks, protection from other predators, and water quality custody. In modern intensive river aquaculture management, water quality prediction plays an important role. The water quality indicator series are nonlinear and non-stationer. Hence, the accuracy of the commonly used conventional methods, including regression analyses and neural networks, were limited. A prediction model based on Smooth Support Vector Machine (SSVM) is proposed in this research to predict the aquaculture water quality. SSVM is an algorithm that is used for solving no linear function estimation problems. The data used in this research are data of river in Surabaya collected for two years. The data have twenty variables that indicate water quality such as temperature, turbidity, color, SS, pH, alkalinity, free CO2, DO, Nitrite, Ammonia, Copper, phosphate, sulfide, iron, Hexavalent Chromium, Manganese, Zinc, Lead, COD, and Detergents. From 520 instance data, we used 5-fold for the experiment. The Root Mean Square Error (RMSE) of the experiment is 0.0275. This value shows that SSVM proven to be an effective approach to predict aquaculture water quality.
Co-Authors Agus Zainal Arifin Akbar, Rizky Januar Alifiansyah Arrizqy Hidayat Anita Hakim Nasution Anny Yuniarti Antonius Kevin Wiguna Arifiani, Siska Arya Yudhi Wijaya Berlian Rahmy Lidiawaty Billy Billy Bilqis Amaliah Darlis Heru Mukti Darlis Herumurti Devanda Sintaullysa Tamba Diana Purwitasari Dini Adni Navastara, Dini Adni Franky Setiawan Daldiri Gulpi Qorik Oktagalu Pratamasunu Imam Kuswardayan Joshua Kevin Rachmadi Kelly Rossa Sungkono Khairun Nisa Laurensius Adi Muhammad Riduwan Nanik Suciati Oktaviandra Pradita Putri Oktaviandra Pradita Putri, Oktaviandra Pradita Rahma Fida Fadhilah Ridho Rahman Hariadi Rully Soelaiman Ryfial Azhar, Ryfial Tiara Anggita Tiara Anggita