Comparison of Supervised Learning Algorithms for Predicting Airline Passenger Satisfaction
DOI:
https://doi.org/10.57152/ijatis.v2i1.1868Keywords:
Decision Tree, K-Nearest Neighbors, Naïve Bayes, Random Forest, Support Vector MachineAbstract
Service quality and airline passenger satisfaction are the main factors in business success in the modern aviation industry. This research compares the performance of supervised learning algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict passenger satisfaction. The k-fold cross-validation method with k=20 was applied to ensure comprehensive model evaluation by dividing the data proportionally. Using a high value of ???? was chosen to optimize the stability of the model estimates, reduce the risk of overfitting, and produce more accurate evaluation metrics. The research results show that the Random Forest algorithm provides the highest accuracy of 95.78%, followed by Decision Tree (93.82%) and K-NN (91.85%). These results indicate that the Random Forest algorithm better classifies passenger satisfaction than other algorithms. This research confirms the potential of machine learning algorithms as a practical solution in data analysis to support strategic decision-making, especially for airlines that want to improve customer experience.
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