Rahmalia, Dinita and Herlambang, Teguh (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). pp. 229-236. ISSN 2503-2267
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Abstract
In Indonesia, the demands of air transportation 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Clustering, Neural Network, Kohonen Network, Fuzzy C Means |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Technique > Program Study of Information Systems |
| Depositing User: | Mr. . Aji |
| Date Deposited: | 29 Jun 2022 06:56 |
| Last Modified: | 29 Jun 2022 06:56 |
| URI: | http://repository.unusa.ac.id/id/eprint/8636 |
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