Development of a Decision Support System for Selecting College Majors Based on an Expert System Using the Forward Chaining Method

Abstract

Purpose of the study: This study aims to develop a decision support system for selecting higher education majors based on users' personality characteristics using the RIASEC theory. The study also aims to analyze the software quality in terms of correctness, functionality, usability, and maintainability and to examine the suitability of system recommendations with users' preferred majors. Methodology: This study employed a Research and Development (R&D) approach with the Waterfall software development model. The system was developed using Visual Basic 6.0 and MySQL. RIASEC theory was used as the knowledge base, while decision tree representation and forward chaining were applied for the inference process. Software quality was evaluated through correctness, functionality, usability, and maintainability testing, involving 60 respondents and the Computer System Usability Questionnaire (CSUQ). Main Findings: The developed system achieved an error/KLOC value of 0 and passed all 34 functionality tests with a 100% success rate. Usability obtained an average score of 72 and was categorized as feasible. All three maintainability aspects instrumentation, consistency, and simplicity meet the criteria. Furthermore, 46 of 60 respondents showed at least one preference matching the system recommendation, resulting in a feasibility rate of 76.67%. Novelty/Originality of this study: The novelty of this study lies in integrating RIASEC personality theory, decision tree knowledge representation, and forward chaining inference into a decision support system for higher education major selection. The study also combines recommendation development with comprehensive software quality evaluation, providing an integrated approach for educational decision support based on personality characteristics.