<![CDATA[Fakultas Teknologi Informasi Universitas Stikubank (UNISBANK) as one of the faculties in higher education in implementing learning activities has produced a lot of stored data and has graduated many students. The level of timeliness of graduation is important for study programs as an assessment of success. This research tries to dig up the pile of student parent data and graduation data in order to get the pass rate and graduation prediction of active students. By implementing the classification data mining technique and the CART algorithm, it is hoped that a decision tree can be used to predict the class timeliness of graduating from active students. By using the graduation data and student parent data totaling 1018 records, a decision tree model was obtained with an accuracy rate of 63% from the data testing test. Determination of split nodes using the Gini Index which breaks the dataset based on its impurity value. Tests conducted in this study show that the order of the variables in the decision tree is gender, origin school status, parental education, age at entry, city of birth, parent's occupation. The prediction with the resulting model is that 71% of active S1 Information Systems students can graduate on time and 51% for S1 Informatics Engineering students.]]>
Copyrights © 2021
