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All Journal Elkom: Jurnal Elektronika dan Komputer
irfan, Irfan Nurdiansyah
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The CLASSIFICATION OF PROSPECTIVE POLICY HOLDERS FOR SELECTING INSURANCE PRODUCTS USING A COMPARISON OF THE K-NEAREST NEIGHBOR METHOD AND THE NAIVE BAYES METHO irfan, Irfan Nurdiansyah; Ari Hidayatullah
Elkom : Jurnal Elektronika dan Komputer Vol 17 No 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1922

Abstract

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.
The CLASSIFICATION OF PROSPECTIVE POLICY HOLDERS FOR SELECTING INSURANCE PRODUCTS USING A COMPARISON OF THE K-NEAREST NEIGHBOR METHOD AND THE NAIVE BAYES METHO irfan, Irfan Nurdiansyah; Ari Hidayatullah
Elkom: Jurnal Elektronika dan Komputer Vol. 17 No. 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1922

Abstract

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.
Co-Authors Ari Hidayatullah