Issue

Vol 3, No 3, August 2018

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

https://doi.org/10.22219/kinetik.v3i3.608
Dinita Rahmalia
Universitas Islam Darul Ulum Lamongan
Teguh Herlambang
Universitas Nahdlatul Ulama Surabaya

Corresponding Author(s) : Dinita Rahmalia

dinitarahmalia@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol 3, No 3, August 2018

Abstract

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.

Keywords

Clustering Neural Network Kohonen Network Fuzzy C Means
Rahmalia, D., & Herlambang, T. (2018). Application Kohonen Network and Fuzzy C Means for Clustering Airports Based on Frequency of Flight. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 3(3), 229-236. https://doi.org/10.22219/kinetik.v3i3.608
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