EFFECTIVENESS TEST OF APPLICATION OF MACHINE LEARNING CLASSIFICATION FOR AIRLINE CUSTOMER SATISFACTION SURVEY X
DOI:
https://doi.org/10.35261/barometer.v8i1.6566Abstract
The use of air transportation is one way that transportation may make it simpler for people to get from one place to another place rapidly. As a result, airlines must enhance the quality of their services by using passengers feedback. Using the data mining method makes it simple to categorize consumer satisfaction from airline surveys. This study focuses on the customer satisfaction classification method created using machine learning with the K-nearest neighbor, decision tree, and random forest models to make it simpler for airlines to categorize. The accuracy, precision, recall, and F1-Score statistics are used to analyze the performance of the classification machine learning model. According to the findings of the performance study, the machine learning decision tree and random forest models have good performance results. The accuracy values for the testing data for the decision tree and random forest models, respectively, are 92,96% and 93,22%. The cross-validation method was also used to examine the two machine learning models to determine which one is more practical to use. The decision tree model and the random forest both have accuracy levels of 96% and 94,5%, respectively, according to the findings of the cross-validation test. Decision trees and random forests can be used to help airline X determine customer satisfaction levels if the cross-validation value is increased.
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References
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