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An Agent Model using Naïve Bayesian for Email Classification

by Muhammad Hasbi, Retantyo Wardoyo, Jazi Eko Istiyanto, Khabib Mustofa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 7
Year of Publication: 2016
Authors: Muhammad Hasbi, Retantyo Wardoyo, Jazi Eko Istiyanto, Khabib Mustofa
10.5120/ijca2016909393

Muhammad Hasbi, Retantyo Wardoyo, Jazi Eko Istiyanto, Khabib Mustofa . An Agent Model using Naïve Bayesian for Email Classification. International Journal of Computer Applications. 140, 7 ( April 2016), 19-23. DOI=10.5120/ijca2016909393

@article{ 10.5120/ijca2016909393,
author = { Muhammad Hasbi, Retantyo Wardoyo, Jazi Eko Istiyanto, Khabib Mustofa },
title = { An Agent Model using Naïve Bayesian for Email Classification },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 7 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number7/24607-2016909393/ },
doi = { 10.5120/ijca2016909393 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:39.672738+05:30
%A Muhammad Hasbi
%A Retantyo Wardoyo
%A Jazi Eko Istiyanto
%A Khabib Mustofa
%T An Agent Model using Naïve Bayesian for Email Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 7
%P 19-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

it is important to carry out email classification to determine its topic [2],[3],[4],[5],[6],[7],[8]. This paper is aimed at making new agents model to determine the email topic by classifying them based on the subject and content autonomously. This domain model is university archiving. The email topic is the keyword of the job description in the university’s units. The email target, except the one to the university director, is based on the email topic. The classification method used was Naive Bayesian and Gaussian Density Methods. The agents used were those with proactive characteristic that can work autonomously in classifying emails. The development of this new model results in the detailed email target. Using this model, most emails can be classified correctly according to the categories.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Agent Email Naïve bayesian Proactive

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