Organization clustering airports using K-Means clustering algorithm

Abstract

Abstract One of the right management quantity of human resources is through the establishment of an efficient organizational structure in accordance with the conditions of the airport. PT. Angkasa Pura II (Persero) is one of the State-Owned Enterprises engaged in the business of airport services in the western region which currently has 16 airports. With the growing needs of air transportation, PT. Angkasa Pura II is projected by the Ministry of Transportation to become the manager of 21 airports. With an additional projection of 5 airports, PT. Angkasa Pura II (Persero) requires projections clustering for all 21 airports to be managed in order to form the right organizational structure. Therefore, 5 clusters are formed using the k-means algorithm. This method is used because it is one of the partitional clustering methods. Partitional clustering method was chosen because it was known that the company wanted to form 5 clusters. In this study clustering was carried out based on variable aircraft movements, passenger movement, cargo, area, terminal area, runway, EBITDA and revenue. The result is obtained in cluster(1) there is 1 airport, cluster(2) there are 6 airports, cluster(3) there are 5 airports, cluster(4) there are 2 airports and cluster(5) there are 5 airports.