<![CDATA[Inclusive student education has become one of the most important agendas of UNESCO and the Indonesian government. Developing an inclusive education for children is critical to adapt to their abilities while attending school. However, most parents and educators who help students select their future secondary school after finishing primary school are often unaware of their real potential. The problem is mainly because the decision is not based on objective assessments such as IQ, average, and mental scores. In this study, our objective is to create a school-type decision support system using data mining as a factor-analytic approach to extract 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. The evaluation showed that the proposed system produced an accuracy of 90% by allocating 75% of the data for training and 25% for testing. The accuracy value from the evaluation phase stated that the ID3 decision tree algorithm performs well. This system can also dynamically create new decision trees based on newly added datasets. More research is expected to result in a more variable and dynamic system that can have a more accurate result for the inclusive student classification of secondary school.]]>
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