Implementation of K-Nearest Neighbors (KNN) Algorithm in Classification of Data Water Quality
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Abstract
Data mining is a process of extracting useful information and patterns from a very large data set. Data mining is also a process of finding useful information that can be used as a supporting tool in decision making. Data that is processed using data mining is able to produce knowledge in accordance with the expectations of technological development. Many techniques can be used in data mining, one of which is data classification techniques. Classification is usually used to obtain patterns or models by going through the process of using existing algorithms. Like the K-Nearest Neighbors algorithm. K-Nearest Neighbors is a case-based reasoning methodology that is trained with a stored case, and can be accessed to perform new solutions. There is a lot of data that can be used in the implementation of classification, but in this study the data used is a collection of water data to determine the quality and quality.
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