Network Disruption Prediction Using Naive Bayes Classifier

被引：0

作者：

Oktaviana, Shinta ^{[1
]}

Ermis, Iklima ^{[1
]}

Anasanti, Mila Desi ^{[2
]}

Hammad, Jehad ^{[3
]}

机构：

[1] Politekn Negeri Jakarta, Dept Comp & Informat Engn, Depok, Indonesia

[2] Imperial Coll London, Dept Med, Sect Genom Common Dis, London, England

[3] Al Quds Open Univ, Dept Comp & Informat Syst, Bethlehem, Palestine

来源：

2019 2ND INTERNATIONAL CONFERENCE OF COMPUTER AND INFORMATICS ENGINEERING (IC2IE 2019): ARTIFICIAL INTELLIGENCE ROLES IN INDUSTRIAL REVOLUTION 4.0 | 2019年

关键词：

network disruption detection; naive Bayes classifier; attenuation; revenue; customer complaint; ODP;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The most crucial challenge of internet service providers is to assure the availability and reliability of their services to their customers. The companies should prevent the customer's complaint by recognizing a potential disruption for the customers, especially in the category 'under spec' condition (potentially impaired service). This study proposed and implemented a model using the Naive Bayes classifier to classify and detect the potential disruption of network services to prevent customer's complaints about their service. The criteria for this model prediction are revenue number of each customer (REVENUE), recurrent disruption value of ODP (N_Q), attenuation value in ODP ( OLT), and attenuation value in customer (ONU). The data classified into three classes or conditions, namely GREEN representing no network disruption, YELLOW is representing low-level disruption, and RED representing high-level disruption, which needs more attention to follow up. The result obtained 91.89% accuracy of the model performance using WEKA Tool.

引用

页码：159 / 163

页数：5

共 20 条

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