A Forward Chaining-Based Expert System for Detecting Stress Levels among Students Writing Their Undergraduate Theses

Abstract

This study aims to design and develop an expert system to assist in identifying stress levels among university students while working on their undergraduate theses using the Forward Chaining method. The research is motivated by the difficulties students face in independently recognizing their stress levels and the limited availability of tools that can provide prompt preliminary information. The study adopts a system development approach consisting of problem identification, data collection and processing, requirements analysis, system design, implementation, and testing. Knowledge of stress symptoms and levels is represented in a knowledge base using IF–THEN rules and processed through Forward Chaining based on the symptoms selected by users. Functional testing was conducted using the black-box testing method to evaluate the application's main features, including loading, registration, login, the main menu, diagnosis, data management, and reporting. The test results documented in the undergraduate thesis indicate that all tested functions operated as expected, achieving a functional success rate of 100%. The developed system can serve as a supporting tool for providing preliminary information about students' stress levels and recommending appropriate coping measures based on the system's results. However, the 100% figure represents the success rate of software functional testing and does not indicate the clinical diagnostic accuracy of the system.