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All Journal Jurnal ULTIMA InfoSys
Carens Chanda Claudhyta Hasan
Institut Teknologi Sepuluh Nopember
Computer Science & IT

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Depression Risk Classification Using Machine Learning: A Model Performance Study Marcelinus Jonathan Salim; Tegar Anugrah Firdaus; Carens Chanda Claudhyta Hasan; Yuri Pamungkas
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4670

Abstract

This study presents a comparative evaluation of multiple machine learning algorithms for depression risk classification using a publicly available mental health survey dataset. Rather than predicting clinical depression, the target variable is formulated as a risk proxy derived from social weakness indicators to support screening-oriented analysis. A quantitative experimental framework is employed to compare Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting under consistent preprocessing and data partitioning conditions. Model performance is evaluated using complementary metrics, including accuracy, recall for High-risk cases, and the area under the receiver operating characteristic curve (ROC-AUC). Threshold optimization based on ROC analysis is applied to align model outputs with screening objectives that prioritize sensitivity. The results demonstrate that Logistic Regression and Support Vector Machine consistently achieve superior or comparable performance across all evaluation dimensions, including high overall accuracy, near-perfect sensitivity for High-risk detection, and strong discriminative capability. In contrast, more complex ensemble and distance-based models show mixed outcomes, indicating diminishing performance gains from increased algorithmic complexity. These findings highlight that simple and interpretable models can effectively support depression risk screening using survey-based data, offering a practical balance between predictive performance, transparency, and computational efficiency.
Redundancy-Aware Feature Selection using mRMR and F-Test for EEG Emotion Classification Ira Febrianti; Carens Chanda Claudhyta Hasan; Nadzifatu Chomtsa; Hanifa Khairunisa; Deandra Faysa Mardatila; Yuri Pamungkas
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4672

Abstract

Emotions play an essential role in human interaction, driving the development of reliable automatic emotion recognition systems. Electroencephalography (EEG) offers a noninvasive method to record neural activity related to emotional states; however, many existing studies focus on limited feature configurations or binary classification problems. This research examines the influence of feature dimensionality and classifier selection on three-class EEG-based emotion recognition involving positive, neutral, and negative categories. The primary contribution of this study is a systematic assessment of feature and classifier compatibility across 28 experimental scenarios within a unified evaluation framework. Using a publicly available EEG dataset containing statistical and spectral features, selection was conducted using F-test and Minimum Redundancy Maximum Relevance (mRMR) methods, isolating the top 5, 10, and 15 features alongside the complete set. Four classifiers (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Neural Networks) were evaluated via a 70/30 hold-out validation scheme using accuracy, F1-score, and Area Under the Curve (AUC). Results indicate that Random Forest trained with the full feature set achieved the highest performance, reaching 99.53% accuracy and 0.9994 AUC. These findings suggest that ensemble-based models demonstrate greater robustness when handling high-dimensional EEG features in multi-class emotion recognition.
Co-Authors Deandra Faysa Mardatila Hanifa Khairunisa Ira Febrianti Marcelinus Jonathan Salim Nadzifatu Chomtsa Tegar Anugrah Firdaus Yuri Pamungkas