Predicting Early Childhood Readiness to Enter Elementary School Using the Naive Bayes Classification - Puspitorini, Sukma; Kahar, Novhirtamely; Kartika, Ikah (Arcitech: Journal of Computer Science and Artificial Intelligence, 2024)
This study aims to examine the readiness and maturity of early childhood in entering elementary school using the Naïve Bayes method. This analysis involves variables such as gender, age, aspects of physical-motor, cognitive, social-emotional development, and literacy skills which include reading, writing, arithmetic, and children's level of independence. The readiness category is classified into two classes, namely "ready" and "not ready". This prediction model is designed to provide a comprehensive understanding of the factors that affect the classification results, so that the evaluation process can be carried out in a transparent, objective, and data-driven manner. This research is expected to be a reference for other educational institutions in implementing a similar model to evaluate student readiness systematically. By adjusting variables and data according to local needs, this model has the potential to support more accurate and standardized decision-making, as well as improve the quality of early childhood preparation in entering formal education. The results show that the Naïve Bayes method is able to achieve an accuracy level of 93.33%, confirming its effectiveness in identifying early childhood readiness optimally.