Expert System for Determining Students Interests and Talents Based on Preference Data Using the Certainty Factor Method

Abstract

Determining students' interests and talents is important in supporting academic success and career planning. However, many students have difficulty identifying their potential due to limited access to expert guidance. This study develops an expert system to determine an expert system to determine students' interests and talents based on technology preference data using Certainty Factor (CF). Data was collected from 100 students of the Information Technology study program who expressed their preference for nine areas of technology, namely: Data Mining, Graphic Design, Educational Information Systems, IOT Technology, Digital Image Processing, CMS Programming, Virtual Reality, Educational Games, and Network Security Systems. The results of the analysis show that CMS Programming is the most in-demand field (94%), followed by Virtual Reality (87%), Digital Image Processing (68%), and Data Mining (64%). On average, students choose 4.79 fields out of nine out of nine available fields. The knowledge base of the system consists of 45 rules of student products into four dominant categories obtained from 3 experts in the fields of educational psychology and information technology. The average combined CF score of all students reached 0.9721, which shows a high level of confidence in determining the talent interest category. The system classifies students into four dominant categories: Data Analytics & Artificial Intelligence (53%), Graphic Design & Multimedia (28%), VR/AR Immersive Technology (11%), and IoT & Embedded Engineering (8%), The results of the system's classification have an 87.5% match with expert assessments, This system is expected to be an academic supervisor's tool in designing the right student competency development program