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All Journal JURIKOM (Jurnal Riset Komputer) Eduvest - Journal of Universal Studies
Dwi Prastya, Ifnu Wisma
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Health Professions Neuroscience Social Sciences

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Comparison of Decision Tree Algorithms and Support Vector Machine (SVM) In Depression Classification In Students Risqi, M. Khoirul; Dwi Prastya, Ifnu Wisma; Vikri, Muhammad Jauhar
Eduvest - Journal of Universal Studies Vol. 5 No. 4 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i4.51108

Abstract

Mental health in adolescents, especially students, is an important concern in the world of education. Early detection of symptoms of depression in students can help preventive efforts in handling them. This study aims to compare the performance of two classification algorithms, namely Decision Tree and Support Vector Machine (SVM) in detecting the level of depression in students based on data obtained from the Kaggle platform. The dataset used consisted of 502 student data with 10 features that caused depression and 1 target class. The research stage includes data preprocessing, which includes data cleaning, categorical value encoding, and normalization with the Min-Max Scaling method. The model was developed using the 5-Fold Cross Validation method to evaluate the classification performance of each algorithm. Model evaluation was carried out using precision, recall, and accuracy metrics. The test results showed that the SVM algorithm had better performance with a precision value of 93.63%, recall of 95.21%, accuracy of 94.22%, and F1-score of 94.68%. Meanwhile, Decision Tree obtained a precision of 81.77%, a recall of 84.90%, an accuracy of 82.86%, and an F1-score of 83.64%. Based on these results, it can be concluded that the Support Vector Machine is superior in classifying depression in students compared to Decision Tree
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network M. Khoirul Risqi; Dwi Prastya, Ifnu Wisma; Barata, Mula Agung
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9420

Abstract

Student attrition presents a major challenge in higher education due to its direct impact on academic quality and institutional graduation rates. Detecting students who are likely to withdraw at an early stage is therefore essential to ensure that timely interventions can be made. This study investigates how three distinct feature selection techniques—Chi-Square, Information Gain, and ANOVA—affect the performance of Artificial Neural Networks (ANN) in classifying student outcomes. The data used in the experiment were drawn from academic and administrative records, which had been standardized through Min-Max normalization. The results demonstrate that each method contributes positively, with classification accuracies ranging from 88.71% to 91.37%. Information Gain emerged as the most effective approach, yielding the highest accuracy at 91.37% and a recall score of 97.29%, largely due to its capability to reduce entropy and isolate the most informative variables. ANOVA also performed consistently well with 90.82% accuracy, while Chi-Square was comparatively less effective, potentially due to its reliance on categorical variables that may not capture predictive nuances. These findings emphasize the strategic importance of applying robust feature selection to improve ANN-based prediction models. Ultimately, this research supports the design of data-driven systems aimed at reducing student dropout rates and strengthening academic retention strategies across higher education institutions.
Co-Authors Jauhar Vikri, Muhammad M. Khoirul Risqi Mula Agung Barata Risqi, M. Khoirul