HRIS with Decision Support for Faculty Appraisal and Promotion
Maria Jackie Lou L. Zinampan
Maria Jackie Lou L. Zinampan, Management Information Systems Office, Cagayan State University, Tuguegarao City, Philippines.
Manuscript received on 18 April 2023 | Revised Manuscript received on 24 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 54-59 | Volume-12 Issue-1, May 2023 | Retrieval Number: 100.1/ijrte.A76020512123 | DOI: 10.35940/ijrte.A7602.0512123
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Of all the resources of the organization, manpower is the only live generating resource. It utilises all other resources, and as a matter of fact, without which none of the different resources would be able to produce anything. Therefore, manpower should be appropriately managed so that they are encouraged to be productive and contribute to the realisation of the organisation’s goals. This study aimed to develop an online decision support system that would aid Cagayan State University in managing 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, simplifying the generation of necessary analytics for decision support. Moreover, the study utilised a classification data mining technique to classify faculty members into appropriate ranks and sub-ranks based on their CCE and QCE points, and to identify faculty members whose rank had not improved in the past 6 years. A 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 management to have, so that necessary interventions can be made to help the faculty member improve and advance. With the use of ISO/IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean of 4.70, qualitatively described as “Very Great Extent”.
Keywords: Classification Technique, Data Mining, Human Resource Management, Information System.
Scope of the Article: Classification