Analysis Of UMN Student Graduation Timeliness Using Supervised Learning Method - Christian Pangestu Kuncoro (IJNMT (International Journal of New Media Technology), 2021)
Education is one of the most important things in human life, and in the world of education. However, there are still many students who graduate not on time. The purpose of this study is to find out an overview of what factors influence, then data analysis, and visualization so that students can graduate on time or not on time for UMN student graduates in 2018-2020. The method or approach used to solve the problem is data collection, independent variable, dependent variable, CRISP-DM, with SQLYog tools, to store data, rapid miner for data cleaning, then calculate prediction accuracy with rapid miner using nave Bayes algorithm, and regression logistics, using the included 10-fold validation method, and visualizing the data with Tableau.