IMPLEMENTATION OF THE RANDOM FOREST METHOD FOR PREDICTING STUDENTS’ LENGTH OF STUDY
DOI:
https://doi.org/10.31258/jsmds.v1i2.15Keywords:
Random Forest, Duration of Study, Cross Validation, Confusion MatrixAbstract
Predicting a student's duration of study is essential for universities to ensure students complete their studies on time. This research aims to develop an effective prediction model for determining the length of study based on related factors. To overcome the complexity and diversity of student data, the Random Forest method was chosen. The results indicate that the Random Forest method is an effective tool for predicting the duration of study for university students. A study was conducted on 1,535 graduates from the five departments at the Faculty of Mathematics and Natural Sciences, Riau University. The study employed cross-validation techniques to measure model performance. The model's accuracy was evaluated using a confusion matrix, which revealed that the Random Forest model had an average accuracy of 95.12%. Additionally, feature importance analyses identified grade point average in the eighth semester as a major contributor to the prediction outcome.
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