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.

[img] Text
RAMA_55201_09021381419104.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (4MB) | Request a copy
[img] Text
RAMA_55201_09021381419104_TURNITIN.pdf - Accepted Version
Available under License Creative Commons Public Domain Dedication.

Download (11MB)
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_01_front_ref.pdf - Accepted Version
Available under License Creative Commons Public Domain Dedication.

Download (1MB)
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_02.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (313kB) | Request a copy
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_03.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (533kB) | Request a copy
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_04.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (814kB) | Request a copy
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_05.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (254kB) | Request a copy
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_06_ref.pdf - Bibliography
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (297kB) | Request a copy
[img] Text
RAMA_55201_09021381419104_0001067709_0006067406_07_lamp.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Public Domain Dedication.

Download (1MB) | Request a copy

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: Mr Alfin Ramdhani
Date Deposited: 31 Jul 2019 02:56
Last Modified: 31 Jul 2019 02:56
URI: http://repository.unsri.ac.id/id/eprint/1358

Actions (login required)

View Item View Item