Application of Artificial Neural Network in Web Expert System for Emotional Preschool Children
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Abstract
This study developed a web-based expert system to assist parents in identifying emotional parenting styles for preschool children using an Artificial Neural Network (ANN). The ANN model consists of an input layer, one hidden layer, and an output layer, using the sigmoid activation function. The parenting styles assessed include authoritarian, democratic, and permissive patterns. The observed symptoms refer to parental behaviors, such as the level of control, frequency of punishment, and involvement in conversations with the child. Data were collected through interviews with psychology experts and literature reviews, resulting in a dataset of 384 samples. The system was developed using the Waterfall software development model. Testing with ten sample cases showed that the system's predictions matched expert evaluations in nine cases (90% accuracy), while one case indicated a discrepancy. These results demonstrate that ANN can effectively classify parenting patterns based on observable symptoms. This expert system offers a practical tool for parents and educators to better understand and apply appropriate parenting strategies, contributing to the emotional development of preschool children.
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