Klasterisasi dan Analisis Trafik Internet Menggunakan Fuzzy C Mean Dengan Ekstraksi Fitur Data
| Main Authors: | Paramita, Adi Suryaputra, Samopa, Febriliyan, Hindayanto, Bekti Cahyo |
|---|---|
| Format: | Article application/pdf |
| Terbitan: |
Jurnal Informatika – Vol.12, No.1 – Mei 2014 – ISSN: 1411-0105 – Progdi Teknik Informatika & Pusat Penelitian Universitas Kristen Putra
, 2014
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| Subjects: | |
| Online Access: |
http://hdl.handle.net/123456789/478 http://puslit2.petra.ac.id/ejournal/ |
| ctrlnum |
123456789-478 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Klasterisasi dan Analisis Trafik Internet Menggunakan Fuzzy C Mean Dengan Ekstraksi Fitur Data</title><creator>Paramita, Adi Suryaputra</creator><creator>Samopa, Febriliyan</creator><creator>Hindayanto, Bekti Cahyo</creator><subject>Traffic, Internet, Fuzzy C-Mean, Clustering, Extraction, feature</subject><description>Internet facilities is one important part of the infrastructure of the campus at this time. Internet facility is a part of teaching and learning activities. Important part of the internet facility is the internet bandwidth, which is often deemed less bandwidth for certain majors at certain hours of lecture hours especially active. To overcome this there needs to be an analysis and clustering of the internet traffic at each point where the distribution of bandwidth is done so that in the end can provide information that can support decision granting bandwidth at each point there. One algorithm for clustering algorithms used are Fuzzy C-Mean, in which the clustering process before the beginning of the internet bandwidth usage data that exists in one period will be collected to be input to the Fuzzy C-Mean algorithm for the distribution of clusters on the use of existing bandwidth based applications that use the internet and network users. But the initial dataset that of the Fuzzy C Mean is not optimal, so we need some optimization dataset using feature extraction data so that the resulting clusters by Fuzzy C Mean algorithm has the accurate output. Results to be obtained from this study is the extraction of feature data that is most appropriate to perform clustering and analysis of Internet traffic based on user applications and the amount of capacity used by the user, which information the clustering results can be used to optimize internet bandwidth</description><date>2014-10-09T01:28:34Z</date><date>2014-10-09T01:28:34Z</date><date>2014-05</date><type>Journal:Article</type><identifier>Paramita, A. S., Samopa, F., & Hindayanto, B. C. (2014). Klasterisasi dan analisis trafik internet menggunakan fuzzy c mean dengan ekstraksi fitur data. Jurnal Informatika , 12(1), 033-039. doi: 10.9744/informatika.12.1.33-39</identifier><identifier>http://hdl.handle.net/123456789/478</identifier><identifier>http://puslit2.petra.ac.id/ejournal/</identifier><type>File:application/pdf</type><publisher>Jurnal Informatika – Vol.12, No.1 – Mei 2014 – ISSN: 1411-0105 – Progdi Teknik Informatika & Pusat Penelitian Universitas Kristen Putra</publisher><recordID>123456789-478</recordID></dc>
|
| format |
Journal:Article Journal File:application/pdf File |
| author |
Paramita, Adi Suryaputra Samopa, Febriliyan Hindayanto, Bekti Cahyo |
| title |
Klasterisasi dan Analisis Trafik Internet Menggunakan Fuzzy C Mean Dengan Ekstraksi Fitur Data |
| publisher |
Jurnal Informatika – Vol.12, No.1 – Mei 2014 – ISSN: 1411-0105 – Progdi Teknik Informatika & Pusat Penelitian Universitas Kristen Putra |
| publishDate |
2014 |
| topic |
Traffic Internet Fuzzy C-Mean Clustering Extraction feature |
| url |
http://hdl.handle.net/123456789/478 http://puslit2.petra.ac.id/ejournal/ |
| contents |
Internet facilities is one important part of the infrastructure of the campus at this time. Internet facility is a part of teaching and learning activities. Important part of the internet facility is the internet bandwidth, which is often deemed less bandwidth for certain majors at certain hours of lecture hours especially active. To overcome this there needs to be an analysis and clustering of the internet traffic at each point where the distribution of bandwidth is done so that in the end can provide information that can support decision granting bandwidth at each point there. One algorithm for clustering algorithms used are Fuzzy C-Mean, in which the clustering process before the beginning of the internet bandwidth usage data that exists in one period will be collected to be input to the Fuzzy C-Mean algorithm for the distribution of clusters on the use of existing bandwidth based applications that use the internet and network users. But the initial dataset that of the Fuzzy C Mean is not optimal, so we need some optimization dataset using feature extraction data so that the resulting clusters by Fuzzy C Mean algorithm has the accurate output. Results to be obtained from this study is the extraction of feature data that is most appropriate to perform clustering and analysis of Internet traffic based on user applications and the amount of capacity used by the user, which information the clustering results can be used to optimize internet bandwidth |
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