PerbandinganOptimasi Feature Selection pada Naïve Bayes untuk KlasifikasiKepuasanAirline Passenger

PerbandinganOptimasi Feature Selection pada Naïve Bayes untuk KlasifikasiKepuasanAirline Passenger

Data Mining, Classification, Naïve Bayes, Particle Swarm Optimization, Genetic Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view ofcustomer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independentassumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independentassumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfactiondata taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfactiondata using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value isAUC of 0.923

Yoga Religia1, Amal

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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