Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)

Abstract— BitTorrent is a P2P file sharing software protocol that allows clients to apply data to other clients and can affect network performance. Bittorent client traffic data collection uses secondary data taken from official sources on the link https://unb.ca/cic/datasets/index.html in 2016. Tra...

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Autores principales:	Hesmi Aria Yanti, Heru Sukoco, Shelvie Nidya Neyman
Formato:	article
Lenguaje:	ID
Publicado:	Universitas Negeri Medan 2021
Materias:	bittorrent c4.5 algorithm correlation based feature selection traffic identification modeling Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/65bb0e8f136b4c7f9c072826ab21009d
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spelling	oai:doaj.org-article:65bb0e8f136b4c7f9c072826ab21009d2021-11-27T05:26:26ZPemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)2502-71312502-714X10.24114/cess.v6i1.20855https://doaj.org/article/65bb0e8f136b4c7f9c072826ab21009d2021-01-01T00:00:00Zhttps://jurnal.unimed.ac.id/2012/index.php/cess/article/view/20855https://doaj.org/toc/2502-7131https://doaj.org/toc/2502-714XAbstract— BitTorrent is a P2P file sharing software protocol that allows clients to apply data to other clients and can affect network performance. Bittorent client traffic data collection uses secondary data taken from official sources on the link https://unb.ca/cic/datasets/index.html in 2016. Traffic data is used as a model for BitTorrent traffic identification using feature-based correlation selection (CFS) and traffic analysis model analysis using Decision Tree Algorithm (C4.5). Feature selection is done to clean irrelevant features so that they can affect the results of the accuracy value. The results of feature selection obtained 7 features and 1 category with 244,689 records and the system connecting the rule tree data training model selected the four best accuracy values. Furthermore, the model training data is carried out by testing the BitTorrent traffic trial data. The results of data testing obtained the best BitTorrent traffic accuracy value of 98.82% with 73,406 records on the 30% data test. Keywords— BitTorrent, C4.5 algorithm, correlation based feature selection, traffic identification, modeling.Hesmi Aria YantiHeru SukocoShelvie Nidya NeymanUniversitas Negeri Medanarticlebittorrentc4.5 algorithmcorrelation based feature selectiontraffic identificationmodelingElectronic computers. Computer scienceQA75.5-76.95IDCESS (Journal of Computer Engineering, System and Science), Vol 6, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	bittorrent c4.5 algorithm correlation based feature selection traffic identification modeling Electronic computers. Computer science QA75.5-76.95
spellingShingle	bittorrent c4.5 algorithm correlation based feature selection traffic identification modeling Electronic computers. Computer science QA75.5-76.95 Hesmi Aria Yanti Heru Sukoco Shelvie Nidya Neyman Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
description	Abstract— BitTorrent is a P2P file sharing software protocol that allows clients to apply data to other clients and can affect network performance. Bittorent client traffic data collection uses secondary data taken from official sources on the link https://unb.ca/cic/datasets/index.html in 2016. Traffic data is used as a model for BitTorrent traffic identification using feature-based correlation selection (CFS) and traffic analysis model analysis using Decision Tree Algorithm (C4.5). Feature selection is done to clean irrelevant features so that they can affect the results of the accuracy value. The results of feature selection obtained 7 features and 1 category with 244,689 records and the system connecting the rule tree data training model selected the four best accuracy values. Furthermore, the model training data is carried out by testing the BitTorrent traffic trial data. The results of data testing obtained the best BitTorrent traffic accuracy value of 98.82% with 73,406 records on the 30% data test. Keywords— BitTorrent, C4.5 algorithm, correlation based feature selection, traffic identification, modeling.
format	article
author	Hesmi Aria Yanti Heru Sukoco Shelvie Nidya Neyman
author_facet	Hesmi Aria Yanti Heru Sukoco Shelvie Nidya Neyman
author_sort	Hesmi Aria Yanti
title	Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
title_short	Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
title_full	Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
title_fullStr	Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
title_full_unstemmed	Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)
title_sort	pemodelan identifikasi trafik bittorrent dengan pendekatan correlation based feature selection (cfs) menggunakan algoritme decision tree (c4.5)
publisher	Universitas Negeri Medan
publishDate	2021
url	https://doaj.org/article/65bb0e8f136b4c7f9c072826ab21009d
work_keys_str_mv	AT hesmiariayanti pemodelanidentifikasitrafikbittorrentdenganpendekatancorrelationbasedfeatureselectioncfsmenggunakanalgoritmedecisiontreec45 AT herusukoco pemodelanidentifikasitrafikbittorrentdenganpendekatancorrelationbasedfeatureselectioncfsmenggunakanalgoritmedecisiontreec45 AT shelvienidyaneyman pemodelanidentifikasitrafikbittorrentdenganpendekatancorrelationbasedfeatureselectioncfsmenggunakanalgoritmedecisiontreec45
_version_	1718409163790352384

Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)

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