Intelligent HRMS incorporates structured decision-support mechanisms that help HR personnel analyze workforce data and make informed decisions. Instead of functioning solely as a digital record-keeping system, the platform integrates rule-based computational models that interpret HR data and generate actionable insights.
The system implements a weighted scoring model for applicant evaluation, allowing HR personnel to objectively rank candidates based on predefined criteria such as education, experience, certifications, and interview results. In addition, an attendance anomaly detection mechanism analyzes employee attendance patterns and assigns risk classifications based on predefined thresholds.
By combining these rule-based models with HR analytics dashboards, the system provides administrators with clear and data-driven insights that support more transparent and efficient human resource management.
System Purpose
Intelligent HRMS is a web-based Human Resource Management System designed to support and automate key HR operations in higher education institutions. The system integrates recruitment management, employee records, attendance monitoring, and leave management within a centralized digital platform.
The project was developed as part of the Master in Information Technology program to demonstrate how structured computational models and decision-support mechanisms can improve the efficiency and transparency of institutional HR processes. By consolidating HR functions into a unified system, Intelligent HRMS enables administrators to manage workforce information more effectively and access real-time operational insights.
Problem Addressed
Many higher education institutions still rely on manual verification processes for critical HR operations such as applicant screening, attendance reconciliation, and leave validation. These manual procedures increase administrative workload, prolong processing time, and limit real-time visibility into workforce data.
For example, attendance verification and payroll preparation may take several days due to manual reconciliation of employee records and attendance logs. As organizations grow and the number of employees increases, manual review processes become less efficient and more prone to errors.
This study addresses these challenges by proposing a centralized web-based HR management system that automates core HR workflows while introducing computational mechanisms that assist administrators in evaluating applicants, monitoring attendance patterns, and validating leave requests.
Decision-Support Concept
Intelligent HRMS incorporates structured decision-support mechanisms that help HR personnel analyze workforce data and make informed decisions. Instead of functioning solely as a digital record-keeping system, the platform integrates rule-based computational models that interpret HR data and generate actionable insights.
The system implements a weighted scoring model for applicant evaluation, allowing HR personnel to objectively rank candidates based on predefined criteria such as education, experience, certifications, and interview results. In addition, an attendance anomaly detection mechanism analyzes employee attendance patterns and assigns risk classifications based on predefined thresholds.
By combining these rule-based models with HR analytics dashboards, the system provides administrators with clear and data-driven insights that support more transparent and efficient human resource management.