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2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Islamabad, Pakistan
Dec. 19, 2016 to Dec. 21, 2016
ISBN: 978-1-5090-5300-1
pp: 137-141
Amri Danades , School of Electrical Engineering and Informatics, Institut Teknologi Bandung Jl. Ganesha No.10, Bandung, Jawa Barat, Indonesia, 40132
Devie Pratama , School of Electrical Engineering and Informatics, Institut Teknologi Bandung Jl. Ganesha No.10, Bandung, Jawa Barat, Indonesia, 40132
Dian Anggraini , School of Electrical Engineering and Informatics, Institut Teknologi Bandung Jl. Ganesha No.10, Bandung, Jawa Barat, Indonesia, 40132
Diny Anggriani , School of Electrical Engineering and Informatics, Institut Teknologi Bandung Jl. Ganesha No.10, Bandung, Jawa Barat, Indonesia, 40132
ABSTRACT
Water is classified into four status of water quality, which good condition, lightly polluted, medium polluted and heavyly polluted. The classification status of water quality is very important to know the proper use and handling. Accuracy in classification of the quality status is very important, so that both of the classification algorithm K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used. The classification of status of water quality based on the parameters. This study discusses the comparison algorithm KNN and SVM in classification of water quality status, a comparison conducted to determine the value that algorithm has the highest accuracy of the determination water Quality Status Classification, testing KNN and SVM algorithm using 10-fold Cross Validation. Based on the result of the test, the highest average value of accuracy is SVM because the accuracy value is higher, it is 92.40% at linear kernel. The average value of KNN accuracy is only 71.28% at K=7.
INDEX TERMS
10 Fold-Cross Validation, Water Quality Status, K-Nearest Neighbor, Support Vector Machine
CITATION
Amri Danades, Devie Pratama, Dian Anggraini, Diny Anggriani, "Comparison of accuracy level K-Nearest Neighbor algorithm and support vector machine algorithm in classification water quality status", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 137-141, 2016, doi:10.1109/FIT.2016.7857553
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