SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student

Authors

  • Mochammad Anshori Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
  • Risqy Siwi Pradini Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
  • Wahyu Teja Kusuma Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

DOI:

https://doi.org/10.21512/emacsjournal.v7i2.13521

Keywords:

self-esteem, machine learning, psychoinformatics, healthinformatics

Abstract

Self-esteem plays a crucial role in students' psychological well-being, influencing their academic performance and personal development. Despite its importance, self-esteem is challenging to measure due to its abstract and subjective nature. This study aims to develop a predictive model to classify students’ self-esteem levels as high or low using machine learning and tabular data obtained through questionnaires. A dataset comprising 47 student responses, with 19 features consisting of social, emotional, demographic aspects, were analyzed. Five machine learning models were evaluated: Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM). To address the class imbalance in the dataset, the study applied SMOTE for data balancing and min-max normalization for feature standardization. Model performance was assessed using accuracy and F1-score. The results reveal that SVM, particularly with an RBF kernel, outperformed other models across all scenarios. On raw data, SVM achieved 66% accuracy and an F1-score of 57.3%. After applying SMOTE, the performance improved to 80% accuracy and a 79.9% F1-score. Further enhancement with normalization resulted in the best performance, with SVM achieving 83.33% accuracy and an F1-score of 83.3%. These results demonstrate how well preprocessing methods work to enhance machine learning models for datasets that are unbalanced. The proposed SVM-based model offers promising applications in educational and psychological settings, enabling early interventions to support students’ mental health.

Dimensions

Plum Analytics

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Published

2025-05-31

How to Cite

Anshori, M., Siwi Pradini, R., & Teja Kusuma, W. (2025). SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student. Engineering, MAthematics and Computer Science Journal (EMACS), 7(2), 175–182. https://doi.org/10.21512/emacsjournal.v7i2.13521
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