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Deanship of Graduate Studies
Document Details
Document Type
:
Thesis
Document Title
:
PERFORMANCE EVALUATION OF EMPLOYEES USING DATA MINING TECHNIQUES TO SUPPORT DECISION MAKING IN HUMAN RESOURCE MANAGEMENT
تقييم أداء الموظفين باستخدام تقنيات التنقيب عن البيانات لدعم صنع القرار في إدارة الموارد البشرية
Subject
:
Faculty of Computing and Information Technology
Document Language
:
Arabic
Abstract
:
Performance evaluation of employees using data mining techniques to support decision making in human resource management Amani Mustafa Y. Ghazzawi Supervised By Dr. Shaimaa Salama ABSTRACT Human resources management needs to understand the factors affecting their employees’ behavior and performance to help organizations make the best decisions and utilize the benefit of their employees’ capabilities. There is a difference in the factors affecting the performance of employees depending on the regulatory environment, whether in the educational or business sectors. Data mining technology is an effective decision support tool that contributes to the analysis and evaluation of employee performance. This thesis aims to improve the performance of faculty members through identification of the factors affecting their performance and prediction of suitable decisions for new faculty members to maximize staff performance and thus achieve higher learning quality. A model based on data mining is developed for the universities sector, which contributes to understanding the factors affecting the performance of faculty members. A K-mean algorithm is applied to faculty data based on specific features of faculty members, to divide them into groups with similar characteristics. Each cluster is analyzed and a decision is recommended. The next step of the model is predicting the decision needed for newcomers depending on the decisions specified on the clustering step. Four classification algorithms (Random Forest, Naive Bayes, K-Nearest Neighbors, Decision Tree) are applied to the data and compared to identify the best resulted performance. The results showed that the random forest algorithm provides better prediction results than other algorithms with an accuracy of 97.86%.
Supervisor
:
Dr. Shaimaa Salama
Thesis Type
:
Master Thesis
Publishing Year
:
1441 AH
2020 AD
Added Date
:
Wednesday, March 11, 2020
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
أماني مصطفى غزاوي
Ghazzawi, Amani Mustafa
Researcher
Master
Files
File Name
Type
Description
46048.pdf
pdf
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