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All Journal Indonesian Journal of Applied Technology and Innovation Science
Abid Zahfran
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparison of Supervised Learning Algorithms for Predicting Airline Passenger Satisfaction Agil Irman Fadri; Abid Zahfran; Taylan Irak; Naufal Helga Firjatullah; Jelita Ekaraya Herianto
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 1 (2025): IJATIS February 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i1.1868

Abstract

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.
Co-Authors Agil Irman Fadri Jelita Ekaraya Herianto Naufal Helga Firjatullah Taylan Irak