Classification of student abilities in reducing students’ drop-out rates
DOI:
https://doi.org/10.12928/bamme.v3i2.10040Keywords:
Education data mining, Reducing drop-out rates, Student ability classificationAbstract
The number of students in the mathematics education study program has declined over the past four years. Besides a few students enrolled in the first year, there are those who have had a temporary leave. These two things are necessary to identify the cause of study leave and need to analyze education data mining (EDM) to obtain a model of classification of students' abilities so that students can improve their achievement. The impact will be a decrease in the number of students undergoing the study. The subject of this study is a student of mathematics education study program from 2008 to 2018. Based on this data set, each student has information about gender, date of birth, home address, postal code, parent's name, secondary school origin, parent job, parent income cell phone number, graduation/drop-out status. The aim of the study is to identify an efficient model between a decision tree, a random forest and a neural network, based on the accuracy of 80 percent decision tree methods, 87 percent random forest methods and 78 percent neural networks.
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