Aplikasi Pengelompokan Mahasiswa Potensial Drop Out Pada STMIK TIME - Sinaga, Triana Melinda (STMIK TIME, 2020)
Collage is an institution that certainly has a large number of databases, such as: academic data, administrative data and student data. Patterns or knowledge in decision making originate from these data if they are explored appropriately. One of the data that can be extracted is the understanding of information on the grouping of potential drop out students. This is important to know and understand. Understanding groupings can be done by understanding and disclosing the knowledge they have. Failure prevention in database management is a very important part of higher education management. The measure of student success or achievement can be seen from the Achievement Index (IP) which reflects all the scores obtained by students until the current semester. With the help of data mining techniques or what is called extracting added value from data in the form of information from a database, such as the Naïve Bayes algorithm, which makes it possible to find the characteristics of student achievement scores using the available data base. A good naive bayes algorithm should ideally produce distinct groups, although in practice perfect separation is usually not achievable. Keyword: Data, Naïve Bayes, Students, Grouping.