An Expert System for Recommendation of Elementary School Readiness Levels for Kindergarten Students Under 7 Years of Age Using the Mamdani Fuzzy Method
DOI:
https://doi.org/10.61536/ambidextrous.v5i03.900Keywords:
Artificial Intelligence, Early Childhood Education, Expert Systems, Fuzzy Inference Systems, School ReadinessAbstract
The readiness of Kindergarten (TK) students under 7 years old to enter Elementary School (SD) needs to be assessed based on various developmental aspects, while the assessment process can face limitations in objectivity and standardized tools. This study aims to build an expert system for recommendations on the level of readiness to enter elementary school using the Fuzzy Mamdani method. The study uses an exploratory quantitative approach with a population of 300 kindergarten student data and 50 data as a test sample. The research instrument is a student development dataset with four indicators, namely writing, reading, communication, and independence. Data are analyzed through fuzzification, IF-THEN rule base, MIN-MAX inference, Centroid defuzzification, and confusion matrix. The results show that the Fuzzy Mamdani baseline obtains an accuracy of 82.00%, a weighted precision of 80.65%, and a weighted recall of 82.00%. The DNN model achieved 92.00% accuracy, while the Hybrid Fuzzy-DNN achieved 94.00% accuracy, 93.80% weighted precision, and 94.00% weighted recall. In conclusion, the Mamdani Fuzzy system can be used as a measurable tool in providing recommendations for student readiness to enter elementary school, while further development is needed to improve classification accuracy in adjacent categories.
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