AnalisisOptimasi Algoritma Klasifikasi Naive Bayes menggunakanGenetic Algorithm dan Bagging

AnalisisOptimasi Algoritma Klasifikasi Naive Bayes menggunakanGenetic Algorithm dan Bagging

Classification, Bank Marketing, Naïve Bayes, Bagging, Genetic Algotithm

he increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Datamining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimizationtechniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%

Agung Nugroho1, Yoga Religia

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/23

Universitas Pelita Bangsa

20 juni 2021

Fajar bagus W

PDF

Indonesia

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