Self-Esteem Classification

This dataset was used in the study entitled “SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student.” Data were collected through a questionnaire distributed to students aged 16–30 years, containing 19 features covering social, emotional, and demographic aspects related to self-esteem levels. A total of 47 responses were obtained, with 64% indicating high self-esteem and 36% indicating low self-esteem. Features include variables such as social relations, psychological well-being, social support, social media usage, emotional regulation, and others. The dataset was used to develop classification models using Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM) algorithms, and evaluated with preprocessing techniques such as SMOTE and min-max normalization. This dataset is suitable for research in psychology, education, mental health, and machine learning, particularly in studies related to psychological prediction using tabular data.

Keywords:
self-esteem, mental health, machine learning, SMOTE, normalization, survey dataset, psychological prediction, Indonesian students

Additional Information:
Sample size: 47
Number of features: 19
Data format: Tabular (CSV/Excel)
Questionnaire language: Indonesian
Measurement scale: Likert 1–5 and categorical data

Citation Suggestion:
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