Saif Kudama Younis
Research InterestsBusiness Administration
Human Resource Management
Strategic Management
Organizational Theory and Organizational Behavior
Quality Management
Marketing Management
Management Information Systems
Information Technology
E-Marketing
E-Commerce
| Gender | MALE |
|---|---|
| Place of Work | Presidency |
| Position | Branch Official of Follow up on Students |
| Qualification | Master |
| Speciality | Management Information Systems |
| saif.younis@ntu.edu.iq | |
| Phone | 07703856611 |
| Address | AlQahirra, Mosul, Mosul, Iraq |
_B.Sc. Management Information Systems (MIS) /Administration and Economic college/Mosul university/Iraq, Mosul (2010).
- Master Degree in Management Information Systems (MIS) /Administration and Economic college/Mosul university/Iraq, Mosul (2013).
19 +
Articles
7 +
h-index
1 +
Scopus Index
251 +
Citations
4 +
Conferences
Languages
اللغة االعربية (100%)
اللغة الانكليزية (80%)
Skills
Computer software packages, Including Microsoft Windows and Office (95%)
Internet and computing core certification (IC3 certification) (95%)
Academic Qualification
Bachelor of Management Information Systems
Nov 5, 2016 - Aug 5, 2010Bachelor of Management Information Systems
Master of Management Information Systems
Sep 5, 2010 - May 15, 2013Master of Management Information Systems
Working Experience
Final Examination Committee [Member of the final examination committee]
Nov 5, 2016 - Aug 31, 2022Member of the Final Examination Committee/ Knowledge University Erbil
Quality Assurance Committee [Member of the Quality Assurance Committee]
Sep 12, 2015 - Aug 31, 2016Member of the Quality Assurance Committee, Department of Finance/ Cihan University
Assistant Lecturer [Assistant Lecturer]
Sep 15, 2013 - PresentAssistant Lecturer
Publications
A Machine Learning Approach to Employee Performance Prediction within Administrative Information Systems
Jan 17, 2024Journal 7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023; Istanbul; Turkey; 23 November 2023 through 25 November 2023; Category numberCFP23JZ6-ART; Code 196776
publisher IEEE
DOI 10.1109/ISAS60782.2023.10391817
Issue category numberCFP23JZ6-ART
Volume Code 196776
This research project delves into the realm of employee performance prediction within organizational contexts, employing the k-Nearest Neighbors algorithm as the core predictive model. The central aim is to optimize organizational success by anticipating variations in individual performance metrics. Key features considered for prediction encompass the assessment of employee performance, average monthly work hours, and tenure with the company. Comparative analyses with alternative methodologies, such as k-Nearest Neighbors (KNN) algorithm, decision trees, and logistic regression, are conducted for a comprehensive evaluation. The dataset undergoes a meticulous 70-30 split for training and testing, resulting in a commendable accuracy of 97.32%. The implications of this research are significant for organizational management, providing a proactive tool to enhance and sustain employee performance, thereby fostering overall organizational success