Implementasi Metode K-Nearest Neighbor Dalam Memprediksi Bau, Rasa Dan Warna Air Sumur Di Kelurahan Selat Baru Bengkalis
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Institusion
Universitas Islam Riau
Author
Sari, Citra Yunela
Subject
QE Geology
Datestamp
2022-03-16 11:36:53
Abstract :
The quality of well water is generally cleaner than surface water because water that seeps into the soil has been filtered by a layer of soil through which it passes. However, based on a survey conducted in the Bengkalis Baru Strait, there are several wells that have poor water quality. Some of them emit odors, saltiness and color that is not clear. Classification of odor, taste and color manually has limited space and time allocation. To solve this problem, this study offers the implementation of the K-Nearest Neighbor method to predict the smell, taste and color of well water. This method works by finding the proximity of a data with other data. The results of testing the best K value obtained at K = 11 and the results of testing the accuracy of the odor, taste and color prediction system is 85% and the implementation results obtained by this system of 75.67% indicate that the smell, taste and color prediction system of this well water can be applied
The quality of well water is generally cleaner than surface water because water that seeps into the soil has been filtered by a layer of soil through which it passes. However, based on a survey conducted in the Bengkalis Baru Strait, there are several wells that have poor water quality. Some of them emit odors, saltiness and color that is not clear. Classification of odor, taste and color manually has limited space and time allocation. To solve this problem, this study offers the implementation of the K-Nearest Neighbor method to predict the smell, taste and color of well water. This method works by finding the proximity of a data with other data. The results of testing the best K value obtained at K = 11 and the results of testing the accuracy of the odor, taste and color prediction system is 85% and the implementation results obtained by this system of 75.67% indicate that the smell, taste and color prediction system of this well water can be applied
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