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All Journal Jurnal ULTIMA InfoSys Jurnal ULTIMA Computing
Yuri Pamungkas
Institut Teknologi Sepuluh November
Computer Science & IT Control & Systems Engineering Engineering

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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.
Early Detection of Non-Melanoma Skin Lesions: A ResNet50 and SVM-Based Deep Learning Approach Ray Louie D’Angelito; Josephine Larissa Rachmadiana; Muhammad Nur Rohman; Timothy Wirjantoro Harjanto; I Nyoman Julian Sanjaya; Yuri Pamungkas
ULTIMA Computing Vol 18 No 1 (2026): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v18i1.4634

Abstract

This study presents a computer-aided detection method for these skin conditions by employing deep learning techniques, specifically a ResNet50-based Convolutional Neural Network (CNN), alongside a Support Vector Machine (SVM) classifier. The aim is to improve diagnostic accuracy and accessibility through image data processing and feature extraction. The main contribution of this research is the application of deep learning for automated classification of non-melanoma skin lesions, with the goal of enhancing early detection. The models were trained and evaluated using the International Skin Imaging Collaboration (ISIC) dataset, with two test scenarios to assess their performance. In Test 4, the CNN demonstrated superior results, achieving F1-scores of 44.70% for actinic keratosis, 85.25% for dermatofibroma, 78.76% for nevus, and a perfect 100.00% for vascular lesion. In comparison, the SVM model achieved lower F1-scores: 21.88% for actinic keratosis, 27.91% for dermatofibroma, 62.46% for nevus, and 70.58% for vascular lesion. The results highlight the effectiveness of deep learning, particularly CNNs, in automated dermatological diagnosis. These findings lay the groundwork for future web and mobile applications that could support early skin cancer detection and clinical decision-making.
Feature Selection Benchmarks for Breast Cancer Diagnosis: A Comparative Machine Learning Study R. Rossa Alfi Nur; Nashir Abbas Husaini; Moch. Arjunnaja; Az-Zahra Batrisyia Juniarto; Yuri Pamungkas
ULTIMA Computing Vol 18 No 1 (2026): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v18i1.4636

Abstract

Breast cancer remains one of the most common causes of death among women, making early and precise detection essential. Yet conventional diagnosis can be limited by specialist shortages, cost, and slow workflows. We therefore assess machine-learning classification with feature selection to streamline diagnosis. Our contribution is a comparative benchmark of feature-selection strategies and classifiers on the WDBC dataset. We evaluated five models (SVM, neural-networks, decision tree, bagged-tree, and boosted-tree). Chi2, mRMR, and ReliefF selected 5, 10, 15, and 30 features, and performance was measured across multiple train–test splits using accuracy, precision, recall, specificity, and F1-score. SVM was overall the top performer and stable across splits. The best SVM setting reached 97.81% accuracy, with strong precision and F1-score, indicating reliable benign–malignant separation. Neural-networks usually ranked second but were more sensitive to the split. Bagged trees generally improved on a single decision tree, while boosted trees showed mixed gains depending on the subset. ReliefF and mRMR often matched or exceeded Chi2 with smaller subsets, showing that careful feature reduction can retain accuracy while lowering dimensionality. In conclusion, combining effective feature selection with an appropriate classifier improves breast cancer classification, and SVM with a compact feature set is a practical choice.
Co-Authors Az-Zahra Batrisyia Juniarto Carens Chanda Claudhyta Hasan Deandra Faysa Mardatila Hanifa Khairunisa I Nyoman Julian Sanjaya Ira Febrianti Josephine Larissa Rachmadiana Marcelinus Jonathan Salim Moch. Arjunnaja Muhammad Nur Rohman Nadzifatu Chomtsa Nashir Abbas Husaini R. Rossa Alfi Nur Ray Louie D’Angelito Tegar Anugrah Firdaus Timothy Wirjantoro Harjanto