Published May 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

HRIS with Decision Support for Faculty Appraisal and Promotion

Creators

  • 1. Management Information Systems Office, Cagayan State University, Tuguegarao City, Philippines.

Description

Abstract: Of all the resources of the organization, manpower is the only live generating resource. It is one that utilizes all other resources and as a matter of fact, without which none of the other resources will be able to produce anything. Being such, manpower should be managed properly in such a way that they would be encouraged to be productive and help in the realization of organization’s goals. This study aimed to come up with an online decision support system that would aide the management of Cagayan State University manage its human resources particularly in terms of appraisal and promotion by providing relevant and timely information. The system is designed to streamline processes in the HR department so as to simplify generation of needed analytics for decision support. Moreover, the study utilized classification data mining technique to classify faculty members to appropriate ranks and sub-ranks based on their CCE and QCE points and identify faculty members whose rank did not improve in the past 6 years. Decision tree was also used to predict the faculty performance based on the three (3) consecutive NBC 461 cycle results. This decision support information is essential for the management to know so that necessary intervention can be made to help the faculty member improve and get promoted. With the use of ISO/IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean 4.70, qualitatively described as “Very Great Extent”.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

Files

A76020512123.pdf

Files (428.4 kB)

Name Size Download all
md5:240a1718b4ce7c9c34de9e9d72011e53
428.4 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2277-3878 (ISSN)

References

  • Mishra, S. N., & Lama, D. R. (2016, May). A Decision-Making Model for Human Resource Management in Organizations using Data Mining and Predictive Analytics. International Journal of Computer Science and Information Security (IJCSIS), 14(5).
  • Mathis, R. L., Jackson, J. H., Valentine, S., & Meglich, P. A. (2017). Human Resource Management. Cengage Learning.
  • Stone, R. J. et.al, (2020). Human Resource Management., 10th edition. John Wiley & Sons Australia.
  • Parry, E. (2009). The benefits of using technology in human resources management. In T. Coronas & M. Oliva, Encyclopedia of human resources information systems: Challenges in E-HRM: 110–116
  • Beadles, N. A., Lowery, C. M., & Johns, K. (2015). The impact of Human Resource Information Systems: An exploratory study in the public sector. Communications of the IIMA, 5(4). https://doi.org/10.58729/1941-6687.1280
  • Patel, J. (2015). "UNDERSTANDING HUMAN RESOURCE INFORMATION SYSTEMS & ITS IMPORTANCE IN ORGANIZATIONS".
  • Nasr, M., Samir, A., Shaaban E., 2019. "A proposed Model for Predicting Employees' Performance Using Data Mining Techniques: Egyptian Case Study". International Journal of Computer Science and Information Security. January 2019
  • Sarker A., Shamim S.M., Zama S., Rahman M. 2018. "Employee's Performance Analysis and Prediction using K-Means Clustering & Decision Tree Algorithm". Global Journal of Computer Science and Technology: Software & Data Engineering. Volume 18 Issue 1 Version 1.0 Year 2018
  • Dhir, K., & Chhabra, A. (2018). Automated empl oyee evaluation using Fuzzy and neural network synergism through IOT assistance. Personal and Ubiquitous Computing, 23(1), 43 52. https://doi.org/10.1007/s 00779 018 1186 6
  • Wang, J. (2021). Innovation of Employee Performance appraisal model based on Data Mining. Lecture Notes on Data Engineering and Communications Technologies, 410–419. https://doi.org/10.1007/978-981-16-5857-0_52
  • Oluwaseun, A., & Chaubey, M. S. (2019). Data Mining Classification Techniques on the. Global Sci J, 7, 79-95.
  • Jantan H., Hamdan A., and Othman Z., (2011). Data Mining Classification Techniques for Human Talent Forecasting, Knowledge-Oriented Applications in Data Mining, Prof. Kimito Funatsu (Ed.),ISBN: 978-953-307-154-1, InTech, Available from: http://www.intechopen.com/books/knowledge- orientedapplications-in-data-mining/data-mining-classification-techniques-for-human-talent-forecasting
  • Yin, Z., & Wang, Z. (2020). Research on Enterprise Performance Evaluation based on Decision Tree Method. Proceedings of the Fifth International Conference on Economic and Business Management (FEBM 2020). https://doi.org/10.2991/aebmr.k.201211.024
  • Chauhan, N. (2020, January 15). Decision Tree Algorithm - Explained. Retrieved September 01, 2020, from https://towardsdatascience.com/decision-tree-algorithm-explained-83beb6e78ef4
  • Sarker A., Shamim S.M., Zama S., Rahman M. 2018. "Employee's Performance Analysis and Prediction using K-Means Clustering & Decision Tree Algorithm". Global Journal of Computer Science and Technology: Software & Data Engineering. Volume 18 Issue 1 Version 1.0 Year 2018
  • ISO 25000 Portal. iso25000.com. (n.d.). Retrieved April 17, 2023, from https://iso25000.com/index.php/en/iso-25000-standards/iso-25010

Subjects

ISSN: 2277-3878 (Online)
https://portal.issn.org/resource/ISSN/2277-3878#
Retrieval Number: 100.1/ijrte.A76020512123
https://www.ijrte.org/portfolio-item/A76020512123/
Journal Website: www.ijrte.org
https://www.ijrte.org
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org