Predicting Anxiety of STMIK Palangkaraya Students Using K-Means Clustering and Gaussian Naïve Bayes

Maura Widyaningsih; Rosmiati Rosmiati; Paholo Iman  Prakoso

doi:10.52436/1.jutif.2026.7.1.5259

Authors

Maura Widyaningsih Program Studi Teknik Informatika , STMIK Palangka Raya, Palangka Raya, Indonesia
Rosmiati Program Studi Sistem Informasi , STMIK Palangka Raya, Palangka Raya, Indonesia
Paholo Iman Prakoso DIKE, Fakultas MIPA, Universitas Gadjah Mada, Yogyakarta, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.5259

Keywords:

Gaussian Naïve Bayes, K-Means Clustering, Machine Learning, Student Anxiety, Student Prediction

Abstract

Academic anxiety is a common psychological problem experienced by students, especially before final exams, which impacts learning performance and mental well-being. This study aims to identify and predict students' anxiety levels using a Machine Learning approach, specifically the web framework Gradio, through a combination of the K-Means Clustering and Gaussian Naïve Bayes (GNB) methods. The research instrument used a Google Form-based questionnaire modified from the Zung Self-Rating Anxiety Scale (ZSAS) with 20 items (K1–K20) on a Likert scale (0–3). Data were obtained from 110 students of the Information Systems and Informatics Engineering Study Program at STMIK Palangkaraya. The research process consisted of five main stages: pre-processing, clustering using the K-Means algorithm, training the GNB classification model, evaluation, and prediction of new data. The clustering results categorized the data into three levels of anxiety: Low, Median, and High. The GNB model showed 95% accuracy with a balanced distribution of evaluation metrics (precision, recall, and F1 score). Comparison with other algorithms shows that while SVM achieved the highest accuracy (100%), GNB was more balanced in handling uneven class distributions and more practical for implementation in web-based systems. This prediction system has the potential to be used as an early detection tool for student anxiety, while also supporting educational institutions in designing more targeted psychological interventions. Further improvements can be made by expanding the scope of respondents, balancing the data distribution, and testing other machine learning methods to improve model generalization. The program and data are available at: https://github.com/maurawidya75/StudentAnxiety2025.

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