Clustering Passenger Satisfaction Levels in Air Travel Using the K-Means Clustering Algorithm
Abstract
This study aims to cluster the satisfaction levels of airline passengers in the business class segment with business travel purposes who are categorized as disloyal, using the K-Means clustering method. The data was sourced from the Airline Passenger Satisfaction dataset on Kaggle, then cleaned, filtered for disloyal business travelers, and transformed into numerical format. The optimal number of clusters was determined using the Elbow Method, which indicated an optimal value at k=3. Clustering was subsequently carried out with the K-Means algorithm and visualized using PCA. Cluster quality evaluation employed the Davies-Bouldin Index, resulting in a value of -0.5, indicating reasonably good cluster separation. These findings can help airlines understand patterns of dissatisfaction among premium customers and design more targeted service strategies to improve their loyalty.
1*, Dwi Hartanti
References
K. Chen, “Research on the Evaluation of Economy Class Service Quality Based on Customer Satisfaction of MU Airlines Degree Programme in Aviation Business,” 2025.
F. K. Nasser and S. F. Behadili, “A Review of Data Mining and Knowledge Discovery Approaches for Bioinformatics,” Iraqi Journal of Science, pp. 3169–3188, Jul. 2022, doi: 10.24996/ijs.2022.63.7.37.
TJ Klein, “Airline Passenger Satisfaction.”
N. F. Syifa, M. Martanto, A. R. Dikananda, and D. Rohman, “APPLICATION OF K-MEANS ALGORITHM IN KINDERGARTEN SCHOOL LOCATION CLUSTERING OF SCHOOL SELECTION STRATEGY BY PARENTS,” Jurnal Komputer dan Informatika, vol. 13, no. 1, pp. 26–35, Mar. 2025, doi: 10.35508/jicon.v13i1.20202.
B. D. Lund and J. Ma Lund, “A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering,” 2021.
S. Suraya, M. Sholeh, and U. Lestari, “Evaluation of Data Clustering Accuracy using K-Means Algorithm,” International Journal of Multidisciplinary Approach Research and Science, vol. 2, no. 01, pp. 385–396, Dec. 2023, doi: 10.59653/ijmars.v2i01.504.
M. Alizade, R. Kheni, S. Price, B. C. Sousa, D. L. Cote, and R. Neamtu, “A Comparative Study of Clustering Methods for Nanoindentation Mapping Data,” Integr Mater Manuf Innov, vol. 13, no. 2, pp. 526–540, Jun. 2024, doi: 10.1007/s40192-024-00349-3.
T. Gambheera Arachchi, M. Dahanayaka, and H. Niles Perera, “Analyzing Sustainability Initiatives of the Airline Industry Through Random Forest Classification and K-Means Clustering Techniques,” 2024.
H. T. Sukmana, “Using K-Means Clustering to Enhance Digital Marketing with Flight Ticket Search Patterns,” Journal of Digital Market and Digital Currency, vol. 1, no. 3, pp. 286–304, Dec. 2024, doi: 10.47738/jdmdc.v1i3.22.
D. Yu, S. Dong, and S. Yao, “Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/3958423.
G. Airlangga, “A Comparative Analysis of Clustering Algorithms for Expedia’s Travel Dataset,” Sinkron, vol. 9, no. 1, pp. 476–483, Feb. 2025, doi: 10.33395/sinkron.v9i1.14343.
