Model student selection using fuzzy logic reasoning approach

In an educational institution, various students criteria contributed to the main reason the student is nominated as a model student. This includes cumulative grade point average (CGPA) of academic courses taken, co-curriculum involvement, soft skills, hard work, leadership, attitude, time management, attendance, attire, and technical skill in order to make the selection decision. The distinctive student is identified firstly based on their overall score. The problem arises when one needs to evaluate all these different criteria in order to select or nominated a model student. It is a tedious task and time consuming for examiner or reviewer to analyze students individually in order to select a student that showed desirable performance. In addition, as a human has its limitation, there is a high degree of error in judgment in decision making. With the aim of bridging this gap, fuzzy logic is applied to imitate human analytic thinking or the ability to make a decision in the model student selection process. Thus, the system is able to assist examiner or evaluator in the decision-making process of determining the model student with minimal time and error. Furthermore, the user could dynamically change the criteria based on the traits that have been set by the system. Fuzzy logic is chosen as it is a powerful mathematical tool involving ambiguous and incomplete information supported by the experience and judgment of examiner or reviewer while combining this cognitive ability in order to reach a verdict. Therefore, the outputs from the experiment conducted shows that the system provides robust output and information to the user regarding student's aptitude and ultimately aim to effectively predict human judgment.