Decision Support System for Faculty Selection, Promotion, and Reclassification Using Predictive Analytics

Abstract

This study aims to design and develop a Decision Support System for Faculty Selection, Promotion, and Reclassification Using Predictive Analytics to replace the inefficiencies of manual processes in higher education institutions. Using logistic regression, the system evaluates faculty performance, tenure, and credentials to ensure fair, data-driven decisions. Guided by Agile Scrum, it was iteratively refined through stakeholder feedback. System testing, based on ISO 25010 standards, showed high ratings in functionality, performance, usability, reliability, security, and maintainability, with an overall weighted mean of 4.56, described as Highly Acceptable. User evaluation via the Technology Acceptance Model (TAM) also indicated strong acceptance, with an overall mean score of 4.45. Overall, the results confirm that the system not only meets international software quality standards but is also positively received by users, highlighting its potential to enhance transparency, accuracy, and data-driven decision-making in faculty selection, promotion, and reclassification.