<![CDATA[The development of information technology has significantly influenced many sectors, including education. Higher education institutions are required to manage and analyze data effectively in order to support decision-making processes. One of the challenges faced by universities is predicting the number of new students each academic year. The uncertainty in the number of applicants can affect academic planning, facility preparation, and marketing strategies carried out by the institution. This study aims to apply the Apriori method to analyze new student admission data in order to discover patterns and relationships within the data that can be used as a basis for forecasting the number of new students in the following academic year. The research method used includes data collection through observation, interviews, and literature study. The data used in this study are historical data of new student registrations from previous years.The analysis process is carried out using the Apriori algorithm to identify frequent itemsets and association rules based on support and confidence values. The results of the study indicate that the Apriori method is capable of identifying patterns and relationships among variables in the new student registration process. The information generated from this analysis can assist universities in developing more effective strategies for student recruitment and admission planning. By implementing a data mining approach using the Apriori method, educational institutions are expected to utilize their existing data to generate valuable information that supports strategic decision making and improves forecasting accuracy for new student admissions]]>
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