Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

Keywords: Data Mining, Klasifikasi, 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 of customer 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 independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, 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 Satisfaction data taken from After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data 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 is AUC of 0.923.


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Author Biography

Yoga Religia, Universitas Pelita Bangsa

In addition to teaching as a lecturer, I also make Startup, which in its activities carry out activities of fostering, training, developing and providing e-commerce services so that MSMEs can enter the digital market and make Darma Nusa Intermedia Start up ( ) which in its activities helps partner companies in conducting HR training and manufacturing information systems. In social activities, I routinely do blood donations and take lessons in one of the majlis groups in Cikarang


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How to Cite
Yoga Religia, & Amali, A. (2021). Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 527 - 533.
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