Fuzzy Logic-Based Decision Support System for Student Admission

This study presents the design and implementation of a Fuzzy Logic-Based Decision Support System (DSS) for student admissions at Christ the King College de Maranding, Inc. (CKCM). Traditional admission processes in many academic institutions rely heavily on rigid thresholds, which often overlook the nuanced characteristics and potentials of student applicants. To address this limitation, the study introduces an intelligent system utilizing a Mamdani-type Fuzzy Inference System (FIS), aiming to provide human-like, objective, and flexible admission decisions. The proposed system evaluates applicants using three primary criteria: Admission Test Score, Interview Rating, and General Weighted Average (GWA). These inputs are converted into fuzzy linguistic variables through fuzzification, followed by the application of a rule-based evaluation framework. A total of ten expert-formulated rules were defined, capturing realistic decision-making behavior based on institutional standards. Defuzzification, using the centroid method, produces a crisp output score categorizing applicants as Accepted, Waitlisted, or Rejected. Real data from CKCM’s 2024 admission cycle were used for system testing. The results indicate that the system is capable of consistently classifying applicants in a manner that aligns well with expert judgment. High-performing candidates were clearly accepted, while borderline or mixed-profile applicants were accurately identified as waitlisted. The fuzzy system also provided clear grounds for rejection, particularly in cases of low academic and interview performance. This system enhances transparency, minimizes bias, and strengthens the integrity of the admissions process. While not designed to replace human judgment, it complements decision-making by providing structured, consistent, and explainable outcomes. The study contributes a practical framework for educational institutions seeking to modernize their admission procedures through artificial intelligence techniques.