<![CDATA[Indonesia, as an archipelagic country, relies heavily on air transportation to support interregional connectivity. Airports play an important role in supporting community mobility, economic activities, and equitable regional development. However, the level of air transportation activity at each airport in Indonesia varies and may change over time. This study aims to cluster airports in Indonesia based on domestic air transportation activity using the K-Means algorithm. The dataset used in this study consists of historical data from the period 2015–2023, including aircraft movements, passenger traffic, baggage volume, cargo volume, and mail shipments obtained from the HUBNET system of the Ministry of Transportation. The analysis process includes exploratory data analysis, data preprocessing, feature selection, and clustering modeling using the K-Means algorithm. The number of clusters is determined using the Elbow and Silhouette methods, resulting in candidate values of K = 2, 3, and 5. The evaluation results show that K = 2 has the best quality with a Silhouette Score of 0.6254, a Calinski Harabasz Index of 3543.93, and a Davies Bouldin Index of 0.5938. However, K = 3 is chosen as it is more representative, consisting of 260 low-activity airports, 1063 medium, and 627 high. Temporal analysis shows 233 cluster transitions across 123 airports, with the highest increase in 2016 (31 airports) and the highest decrease in 2018 (23 airports). The results indicate that airport activity is dynamic and that clustering can be used as a basis for data-driven airport planning and evaluation.]]>
