The CLASSIFICATION OF PROSPECTIVE POLICY HOLDERS FOR SELECTING INSURANCE PRODUCTS USING A COMPARISON OF THE K-NEAREST NEIGHBOR METHOD AND THE NAIVE BAYES METHO
DOI:
https://doi.org/10.51903/elkom.v17i1.1922Keywords:
Data Mining, Classification, K-Nearest Neighbor, Naïve Bayes, Insurance Product SelectionAbstract
The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.
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