Application Of Certainty Factor In Detecting Learning Concentration Disorders In Children At School

Abstract

Learning concentration disorders are a fairly common problem in elementary schools and can have a significant impact on students' academic performance. At UPTD SDN 015861 Bunut Seberang, initial observations indicate that around 20–25% of students experience symptoms such as distractibility, difficulty focusing, hyperactivity, and decreased motivation to learn. Current interventions are limited, lack a structured system, and rely on teachers' subjective assessments. This study aims to develop an expert system based on the Certainty Factor method to detect learning concentration disorders more objectively and accurately. The research method uses a quantitative approach with stages of problem identification, data collection through observation and interviews, analysis, system design using UML, and implementation based on PHP and MySQL. The system processes student symptom data, calculates the confidence level of the diagnosis, and provides follow-up recommendations for teachers and parents. The implementation results show that the system can efficiently identify potential learning concentration disorders, assist teachers in decision-making, and serve as a model that can be adapted by other schools facing similar issues.