KOMBINASI METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES UNTUK KLASIFIKASI DATA

SARI, MEGA KARTIKA (2015) KOMBINASI METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES UNTUK KLASIFIKASI DATA. S2 thesis, UAJY.

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Abstract

Data mining is widely used to help determine the decision to predict the future trend of the data. Data mining can be implemented in various fields such as education, health, marketing, insurance, etc. Modification of the algorithm has been raised by many researchers to combine several data mining methods. Merging data mining methods are used to improve the process of data classification by recognizing the drawback of a data mining method. The drawback of the method then, can be solved by another method. This research will discuss about merging two data mining methods to classify the data. The combined classification methods used in this research are KNN and Naïve Bayes. These methods are in the top 10 frequently used data mining. KNN has a drawback in the data classification phase which will be fixed by Naïve Bayes. Application of this two combined method, KNN and Naïve Bayes, will accelerate data mining process with high accuracy values such as KNN. This combined method will be applied using C ++ programming language.

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