Early Detection of Student Problems Through a Knowledge-Based Systems-Based Counseling Approach

Higher education is an important phase in an individual's academic development, but it is often characterized by challenges such as academic pressure, time management, and student mental health. To overcome these problems, this research aims to develop a technology-based Counseling Expert System with a Forward Chaining approach to detect student problems and provide relevant solutions. The system is designed and implemented as a web-based platform that can be accessed anytime and anywhere, allowing students to answer questions related to the problems faced by students. The answers are processed in a knowledge base that is integrated with an inference engine to produce diagnosis and solution recommendations. The results of system testing using 30 data samples show results that are in accordance with expert judgment. This expert system can identify six types of student problems, such as laziness, skipping classes, adaptation difficulties, difficulty doing final assignments, decreased Grade Point Average (GPA or IP), and potential dropout, by considering 32 causal factors grouped into academic, time management, emotional, and social environment categories. This research proves that the Forward Chaining-based Counseling Expert System is effective as a flexible solution to support student well-being and better student academic achievement.