<![CDATA[Admission of new students is an essential activity for universities. As the functional process of student admissions progresses, data on student admissions increases from one year to the next. However, the new student admission data has not been used by universities for decision-making, the potential for promotion, and consideration of the admission path. This study aims to classify student data into several groups considering the proximity of the data has similarities. So that student data who have the exact attributes are collected in one group, and those with different features are contained in another group, using a data mining process, namely the k-means smart clustering strategy. The business intelligence system in this study expands institutional excellence by using various data and information driven by institutions as an ingredient in the decision-making cycle. The method used by the author is knowledge discovery in databases (KDD) which consists of Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, and knowledge. Tests are carried out using RapidMiner tools to help find the right results to overcome these problems. Finally, the results of this study are used as a reason to pursue choices in decision-making and determine promotion strategies based on groups formed from institutions.]]>
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