Classification of Secondary School Destination for Inclusive Students using Decision Tree Algorithm

Abstract

Inclusive student education has become one of the most important agendas for UNESCO and the Indonesian government. Developing inclusive children's education is critical to adjust their abilities while attending school. However, most parents and educators who assist students in selecting their future secondary school after finishing primary school are frequently not aware of their genuine ability. The problem is mainly because the decision is not based on objective assessments like IQ, average and mental scores. In this study, we aims to create a school-type decision support system using data mining as a factor analytic approach in extracting rules for the knowledge model. The system uses some variables as the basic principles for building school-type classification rules using the ID3 decision tree method. This system can also assist educators in making decisions based on existing graduate data. Evaluation showed that the proposed system produced an accuracy of 90% by allocating 75% of data for training and 25% for testing. The accuracy value from evaluation phase stated that the ID3 Decision tree algorithm have a good peformance. This system also can dynamically create new decision trees based on newly added datasets. Further research is expected to have more variable and more dynamic system that can have more accurate result for the inclusive student classification of secondary school.