Every university has objectives to make sure their students graduate on time. This objective can be achieved by using early warning system (EWS). Through EWS, students who will graduate late can be recognized in advance. Thus, appropriate interventions can be given to the student so that they can graduate on time. The predictive model is the core of an EWS, that built based on the graduated student data. The problem that often arises in a predictive model is the degree of accuracy. In order to increase the accuracy of the prediction, the clustering of attribute selection need to be conducted first. One of approach that can be used to cluster attribute selection is by using Maximum Degree of Domination in Soft Set Theory (MDDS) algorithm. This article implements the MDDS algorithm to cluster the attributes from student datasets. The results obtained from this research is the dominant attributes that can be used as a foundation to develop a predictive model of student graduation time.
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