| dc.description.abstract | The school dropout rate is a crucial indicator for assessing the quality and equity of
education in a region. A high dropout rate often reflects structural issues such as
poverty, limited access to educational facilities, and a shortage of teachers, which
ultimately lead to a decline in the quality of human resources. This condition requires
an analytical method capable of identifying patterns and accurately predicting the level
of dropout risk. This study aims to classify the dropout risk level in North Sumatra
Province using the Naive Bayes classification algorithm, an effective probabilistic
model for categorical prediction based on numerical data. The model was built using
several independent variables, namely the poverty rate, population density, number of
schools, number of teachers, student-to-school ratio, student-to-teacher ratio, number
of male and female students, and the school dropout rate. These variables were
selected because they represent socio-educational factors that influence the continuity of
education. The classification process was carried out using the Gaussian Naive Bayes
approach, which assumes that each feature follows a normal distribution within each
class, thus enabling probabilistic predictions with high efficiency. The results show that
the model achieved a prediction accuracy of 87.88% and effectively classified regions
into “High” and “Low” school dropout risk categories. | en_US |
