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Wahyu Tejakusuma, Wahyu
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
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Journal : engineering mathematics and computer science journal emacs

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SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student Anshori, Mochammad; Siwi Pradini, Risqy; Teja Kusuma, Wahyu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13521

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.
Co-Authors Achmad Fariz Agung Hadi Endaryanto Agung Setia Budi, Agung Setia Agus Purnomo Rijal Kurniawan Ahmad Afif Supianto Ahmad Afif Supianto Ahsanun Naseh Khudori Ahsanun Naseh Khudori Ainun, Nurul Andy Setyawan Anwar, David Saiful Bagus Prasetyo Abdi , Benben Binarti Widyanti Binti Karimatun Nikmah Carrina A. Tesa Choirunnisa, Rosyida Danang Bagus Baskoro Devi Ayu Trisnaningrum Dio Putra Mahardhika Djajalaksana, Susanthy Dyah Utami, Kartika Eko Sakti Pramukantoro, Eko Sakti Emi Aprilyanti Farah Aulia Nisa Faurika Febriantama, Mohammad Dhany Febry Andrias francisca, khollydayyana mitra Hargiani, Fransisca Xaveria Henokh Kristiawan Heri Wahyu Wibowo Herman Tolle Javier Fajri Zachary Leny Candra Kurniawan Lili Octaviani Lola Novira Risdiyanti M. Syauqi Haris Magdalena Amelia Rizky Mochammad Anshori Nindynar Rikatsih Novianto Agus Prabowo Nugroho Teguh Yuono Nurul Halimah Pitunas, Hery Pradini, Risqy Siwi Pradita, Angria Prasetiyo, Bagus Dadang Provisa Redy Fernando Putrinugroho, Ivana Aikozora Retno Widaningrum Rikatsih, Nindynar Risa Puspita Sari Risqy Siwi Pradini Sartoyo Savril Daffa Geraldo, Jeffana Setiawan Edi Kriswanto Shaktyanti, Frenchyani Anggi Silvia Intan Wardani Sri Lestari Wati Supatmi, Eny Usfania Fernanti Vanesa Putri Maya Hasyim Vincentia Kristanti Wardoyo, Puspo Wibowo, Heri Wahyu Wirpan Atmaja Putra Yuono, Nugroho Teguh