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Data Mining for Engineering Student Success by Nur Afni Septiani is a document available to read on EtoBox.

What is Data Mining for Engineering Student Success about?

This document discusses using data mining techniques to predict student performance in engineering programs. Specifically, it applies three decision tree algorithms - C4.5, ID3, and CART - to classify students as likely to pass, fail, or need to repeat a year based on their data. The results aimed to identify weaker students and help them improve their performance. After exams, the predicted and actual results were analyzed to evaluate the models

Author

Nur Afni Septiani

Language

EN