Implementation of evaluation, planning, and decision making can be done better if an organization has complete, fast, precise, and accurate information. The required information can be retrieved from operational data stored in the integrated database. Data mining has an important role in everyday life. By understanding its definitions, functions, methods and applications, it will be easier to put them into practice. In addition, data collection is also very much needed in various fields of life ranging from telecommunications, insurance, sports, finance, academic fields and other fields. In-depth understanding of data mining is needed to simplify the work. This study examines the extraction of operational data and then analyzes the data using data mining techniques. Data Mining is the process of analyzing data using software. To find certain patterns or rules from a large amount of data that is expected to find knowledge to support decisions. This study uses student data which includes data on National Examination scores, written test scores, interest and aptitude test scores to determine student majors. In this study, the data mining technique used is Classification. The way to do the classification is by using data mining techniques using machine learning. In this study, the data mining technique used is classification using the CRISP-DM method and modeling by comparing the four models namely Decision Tree, Naïve Bayes, KNN Classification and Random Forest with Rapidminer tools to help find characteristics or variables that support in determining student abilities. . Furthermore, it can be used for future student majors. From the results of the analysis that has been done, the model using Decision tree has an accuracy of 95.58%, Naïve Bayes 88.97%, KNN Classification 93.54% and Random forest has an accuracy of 96.46%. The final conclusion is that modeling with Random Forest can be used to help determine student majors in the best private catholic high school on the island of Flores.