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Naive Bayes Optimization Based On Particle Swarm Optimization to Predict the Decision of Insurance Customer Candidate

Journal: International Journal of Computer Techniques (Vol.5, No. 5)

Publication Date:

Authors : ;

Page : 8-14

Keywords : Insurance; Mining Data; Classification; Naive Bayes; Particle Swarm Optinmization;

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In order to deal with business competition and increase the company's revenue, both company leaders and management in a company are required to be able to make the right decisions in determining the sales strategy. To be able to do this, companies need a large amount of information to be analyzed further. The company executive expects technology that can produce information that is ready to be used to assist them in making corporate strategic decisions. They want to know what products should be improved, how much the results obtained by the company will be achieved. To meet the needs of entrepreneurs above, there are many ways that can be pursued. One of them is by utilizing corporate data. Based on the identification of the problems that have been described, the problem is formulated as follows: how the optimization results for the Naive Bayes algorithm based on Particle Swarm Optimization, how to build a prototype to predict customer data by using the appropriate data mining classification method. This study aims to recommend a prototype of the data mining classification method for potential customer predictive data in offering an existing product based on data mining classification method, namely the Naïve Bayes algorithm based on Particle Swarm Optimization. This study, if viewed from the form of data and information that is managed, this research is classified as a quantitative type of research. Quantitative research is a research whose hypothesis can be tested by statistical techniques. This method is used when conducting quality testing using the k-fold cross validation method (k = 10) which displays the value of accuracy, precision, recall, ROC for each method that is compared. The results of application development and model performance measurement will be explained in this section. Application development will be discussed in testing to show that the results of the application are made as expected. Whereas in the measurement of model performance will be explained the results of the model performance measurement for the analysis of potential customers and potential customers not to be offered insurance products at PT. XYZ by using Naive Bayes based on Particle Swarm Optimization, the results obtained in the algorithm that has been optimized are made an application developed.

Last modified: 2018-10-01 23:57:18