KLASIFIKASI TRAFFIC NETWORK DENGAN MENGGUNAKAN NAIVE BAYES DAN FEATURE SELECTION DALAM PEMILIHAN ATRIBUT

RAMDHANI, ALFIN and Primartha, Rifkie and Sazaki, Yoppy (2019) KLASIFIKASI TRAFFIC NETWORK DENGAN MENGGUNAKAN NAIVE BAYES DAN FEATURE SELECTION DALAM PEMILIHAN ATRIBUT. Undergraduate thesis, Sriwijaya University.

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Abstract

Now the number of users of Internet services is increasing. So, the more traffic on the internet is also congested. How to get the utilization traffic pattern is through the traffic classification process. Because the volume of traffic log data is very large and always increasing rapidly, it requires an effective and simple method to be applied in the classification process. So the Naive Bayes method is chosen, which is pretty much applied to calculating the probability level. And using Feature Selection in the selection of attributes that appears. This study uses internet traffic data with 248 attributes. In this research, the results of the Naive Bayes classification based on class with the highest value of accuracy, precision, and recall in the WWW class are (0.994, 0.91, 0.994) and the results of the feature selection using the ranking feature method are the highest entropy value 0.86914856 for attribute 4. The highest value attribute validation on attribute 35 is 97.2942%. The results obtained from this study are expected to be useful for decision makers for managing the internet in the future.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Feature Selection, Internet Traffic, Naive Bayes, Classification.
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150-4380 Computer network resources
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 212 not found.
Date Deposited:	31 Jul 2019 02:56
Last Modified:	31 Jul 2019 02:56
URI:	http://repository.unsri.ac.id/id/eprint/1358

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