September 25, 2025Jurnal Pilar Nusa MandiriOpen Access

Comparative Analysis of Random Forest and Support Vector Classifier for Predicting Students’ on-Time Graduation

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The Random Forest algorithm achieved an accuracy of 91.86% in predicting on-time graduation, outperforming the SVC model.
Key metrics assessed included accuracy, F1 score, and ROC-AUC, highlighting significant performance differences between the algorithms.
The dataset comprised 1,298 student records, incorporating various academic data points relevant to graduation.
The findings enable the development of early detection systems that can assist in creating solution-oriented academic policies.

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Implication

This analysis compares random forest and support vector classifier models for predicting on-time graduation rates, implying potential improvements in academic management.

Cite This Study

Nurus Sarifatul Ngaeni (2025) studied this question.

synapsesocial.com/papers/68d5bd72dc445aa9033b0aee https://doi.org/10.33480/pilar.v21i2.7048

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Implication

This analysis compares random forest and support vector classifier models for predicting on-time graduation rates, implying potential improvements in academic management.

Comparative Analysis of Random Forest and Support Vector Classifier for Predicting Students’ on-Time Graduation

Key Points

Discussion

Implication

Cite This Study

Comparative Analysis of Random Forest and Support Vector Classifier for Predicting Students’ on-Time Graduation

Key Points

Discussion

Implication

Cite This Study

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Also Consider