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All Journal JOIV : International Journal on Informatics Visualization
Rohman, Muhammad Ghofar
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Hybrid Logistic Regression Random Forest on Predicting Student Performance Rohman, Muhammad Ghofar; Abdullah, Zubaile; Kasim, Shahreen; Rasyidah, -
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3972

Abstract

The research aims to investigate the effects of unbalanced data on machine learning, overcome imbalanced data using SMOTE oversampling, and improve machine learning performance using hyperparameter tuning. This study proposed a model that combines logistic regression and random forests as a hybrid logistic regression, random forest, and random search SV that uses SMOTE oversampling and hyperparameter tuning. The result of this study showed that the prediction model using the hybrid logistic regression, random forest, and random search SV that we proposed produces more effective performance than using logistic regression and random forest, with accuracy, precision, recall, and F1-score of 0.9574, 0.9665, 0.9576. This can contribute to a practical model to address imbalanced data classification based on data-level solutions for student performance prediction.
Co-Authors Abdullah, Zubaile Rasyidah, - Shahreen Kasim, Shahreen