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In Indonesia, the demands of air tranportation for reaching destination increase rapidly. Based on the flight schedule in airports spreading in Indonesia, the airports have different flight demand rate so that it requires clustering. This research will use two methods for clustering : kohonen network and Fuzzy C Means (FCM).Kohonen network is the type neural network which uses unsupervised training.Kohonen network uses weight vectors for training while FCM uses degree of membership. Both kohonen network and FCM, inputs are represented by the number of departure and arrival of airline in one day. For kohonen network, we update weight matrices so that minimizing the sum of optimum euclidean distance. For FCM, we update degrees of membership so that minimizing the objective function value.From the simulations, we can cluster the airports based on the number of departure and arrival of airline.

Application Kohonen Network and Fuzzy C Means for Clustering Airports Based on Frequency of Flight