Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation
River water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy.
Trisnawati Adi and Masduqi Ali, Analysis of Quality and Strategy of Control of Surabaya River Water Pollution. Purification Journal. Vol. 14, No. 2, pp.90-98, 2014. DOI: https://doi.org/10.12962/j25983806.v14.i2.14
Arief Muchlisin, Mapping of Suspended Solids Using Landsat Satellite Data, Remote Sensing Journal. Vol. 9 No. 1, pp.67-75, 2012.
Thesa Septine Citri Priyono, Emma Yuliani, Rini Wahyu Sayekti, Study of Determination of Water Quality Status in Surabaya River for the Purpose of Drinking Water Raw Materials, Journal of Water Resources Engineering, Volume 4, Number 1, 2013.
Ihya Ulumuddin, Surabaya River Water Quality is Below the Quality Standards, Sindo Newspaper, accessed on January 30, 2018.
Ayu Ratri Wijayaning Hakim and Yulinah Trihadiningrum, Brantas River Water Quality Study Based on Macroinvertebrates, Journal of the Science and Arts of Pomits, Vol. 1, No. 1, 2012 .
Fawaz Al-Badaii, Mohammad Shuhaimi-Othman, and Muhd Barzani Gasim, Water Quality Assessment of the Semenyih River Selangor Malaysia, Hindawi Publishing Corporation Journal of Chemistry Volume 2013. DOI: https://doi.org/10.1155/2013/871056
Sri Rahmawati F, M. Isa Irawan, Nieke Karnaningroem, Pattern of Pollutant Distribution in Surabaya River Using Kohonen Network, Proceedings of the Environmental Technology Seminar, ISBN 978-602-95595-9-0, 2014.
Muh Faisal Dinniy, Ali Ridho Barakhbah, Entin Martiana Kusumaningtyas, Quality Measurement Classification for Water Treatment using Neural Network with Reinforcement Programming for Weighting Optimization, Knowledge Creation and Intelligent Computing (KCIC), 2016. DOI: https://doi.org/10.1109/KCIC.2016.7883636
MA Huiqun and LIU Ling, Water Quality Assessment using Artificial Neural Network, International Conference on Computer Science and Software Engineering, DOI 10.1109/CSSE.2008.411, IEEE, 2008.
Sofi Defiyanti and Mohamad Jajuli, Integration of Classification and Clustering Methods in Data Mining, National Informatics Conference 2015.
K. Sumathi, S. Kannan,K. Nagarajan, Analysis of student database using Classification Techniques, International Journal of Computer Applications (0975 – 8887), Volume 141 – No.8, May 2016. DOI: https://doi.org/10.5120/ijca2016909703
Moussa Demba, Algorithm For Relational Database Normalization Up To 3nf, International Journal of Database Management Systems ( IJDMS ) Vol.5, No.3, June 2013. DOI: https://doi.org/10.5121/ijdms.2013.5303
G.P.Zhang, Neural networks for classification, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Volume: 30 , Issue: 4 , Nov 2000. DOI: https://doi.org/10.1109/5326.897072
Zhenjun Li, A Data Classification Algorithm of Internet of Things Based on Neural Network, iJOE ‒ Vol. 13, No. 9, 2017. DOI: https://doi.org/10.3991/ijoe.v13i09.7587
Saravanan K and S. Sasithra, Review on Classification Based on Artificial Neural Networks, International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, December 2014. DOI: https://doi.org/10.5121/ijasa.2014.2402
Stephan Dreiseiti, Lucila Ohno-Mchado, Logistic Regression and Artificial Neural Network Classification Models, Journal of Biomedical Informatics, Volume 35, Issues 5–6, Pages 352-359, October 2002. DOI: https://doi.org/10.1016/S1532-0464(03)00034-0
Mesterjon, Building Design Implementation Using Google Sketchup 8 Application, Journal of Infotama Media Vol.8 No.2, 2012.
Bayu Rahayudi, Marji, Data Mapping and Water Depth Visualization in Dams or Reservoirs, Journal of Information Technology and Computer Science (JTIIK) p-ISSN: 2355-7699 Vol. 4, No. 2, pp. 111-116, 2017. DOI: https://doi.org/10.25126/jtiik.201742305
Kawa Nazemi, Dirk Burkhardt, Egils Ginters, Jorn Kohlhammer, Semantics Visualization – Definition, Approaches and Challenges, 2015 International Conference on Virtual and Augmented Reality in Education, Procedia Computer Science 75 – 83, 2015. DOI: https://doi.org/10.1016/j.procs.2015.12.216
Dewi Agushinta R, Representing Knowledge Within Human Interaction And Computers, PESAT National Seminar, Gunadanna University, jakarta, ISSN : 18582559, 2005.
Ferihane Kboubi, Anja Habacha Chaibi and Mohamed BenAhmed, Semantic Visualization and Navigation Intextual Corpus, International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.1, January 2012. DOI: https://doi.org/10.5121/ijist.2012.2105
Kawanazemi, Adaptive semantics Visualization, Springer international publishing switzerland, 2016.
Yanmin Sun, Andrew K. C. Wong and Mohamed S. Kamel, Classification of Imbalanced Data, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23, No. 04, pp. 687-719 (2009). DOI: https://doi.org/10.1142/S0218001409007326
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