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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Chairil Umri
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Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering

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Comparison of Machine Learning Classification Algorithm Performance for Depressive Symptom Recognition in College Students Arinda Aulia; Falah Affandi; Puan Syaharani Sitorus; Chairil Umri; Ferizal Fadli Tanjung; Mhd. Furqan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1998

Abstract

College students are vulnerable to depressive symptoms due to academic, social, and personal pressures, which can impact mental health and academic achievement. Early detection is necessary to prevent this condition from developing into a more serious condition, but conventional methods often lack objectivity. With the development of artificial intelligence, machine learning classification algorithms offer a more accurate approach to recognizing patterns of depressive symptoms. This study compared the performance of several classification algorithms, namely Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine, using a dataset of depressive symptoms in college students. Evaluation was carried out based on accuracy, precision, recall, and F1-score. The results showed that Logistic Regression achieved the best performance with an accuracy of 95.62%. This suggests that selecting the right algorithm can improve the effectiveness of early depression detection systems in college students and support data-driven mental health efforts.
Optimization of HIV/AIDS Classification Using the SMOTE Technique and CatBoost Algorithm Annisa Fadhillah Pulungan; Chairil Umri; Rossy Nurhasanah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18134

Abstract

Despite various mitigation efforts, Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS) remains a significant public health issue with widespread impacts in Indonesia. One of the challenges in HIV/AIDS classification using machine learning is data imbalance, where the number of HIV cases is smaller than Non-HIV cases. The aim of this study is to analyze the performance of the CatBoost algorithm in classification tasks and to evaluate the impact of the Synthetic Minority Oversampling Technique (SMOTE) on improving model performance in imbalanced datasets. The research method involves applying the CatBoost algorithm to the original dataset as well as to data that has been processed using SMOTE-based oversampling. Furthermore, model performance is evaluated using Precision, Recall, F1-Score, and Precision-Recall Area Under Curve (PR-AUC) metrics. The SMOTE + CatBoost model achieved an accuracy of 95%, precision of 93%, recall of 92%, F1-Score of 93%, and PR-AUC of 0.953, all of which are higher than those of the CatBoost Baseline model. In addition, the number of undetected HIV cases was reduced from 28 to 13 cases. The findings indicate that the integration of SMOTE with the CatBoost algorithm improves model performance, resulting in better classification outcomes on imbalanced datasets compared to the CatBoost Baseline, and potentially supports a more effective HIV/AIDS early detection system.
Co-Authors Annisa Fadhillah Pulungan Arinda Aulia Falah Affandi Ferizal Fadli Tanjung Mhd. Furqan Puan Syaharani Sitorus Rossy Nurhasanah