Open AccessDevelopment of a Job Applicants E-government System Based on Web Mining Classification Methods
Author(s) -
Rasha Hani Salman,
Nadia Adnan Shiltagh,
Mahmood Zaki Abdullah
Publication year - 2021
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2021.62.8.28
Subject(s) - c4.5 algorithm , naive bayes classifier , seekers , decision tree , computer science , government (linguistics) , data mining , statistical classification , logit , machine learning , data science , support vector machine , political science , linguistics , philosophy , law
Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicated that the algorithm j48 had the highest precision (94.80%) compared to other algorithms for the aforementioned dataset.