Clustering Passenger Satisfaction Levels in Air Travel Using the K-Means Clustering Algorithm

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