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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rudy Herteno
Department of Computer Science, Faculty of Mathematics and Natural Science, Lambung Mangkurat University, Banjarbaru, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Depression Level Classification Using Compact Cross-Domain Feature Engineering on Sleep, Physical Activity, and Demographic Data Nila Yoga Tama Nurwati; Fatma Indriani; Friska Abadi; Dodon Turianto Nugrahadi; Rudy Herteno
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.356

Abstract

Depression is a common mental health disorder and a major public health concern, and early identification of depressive symptoms using population survey data can support exploratory risk analysis. However, many previous studies formulated depression prediction as a binary classification task. They used broad predictor sets, while multiclass depression-level classification with compact and interpretable cross-domain features remains less explored. This study developed a compact cross-domain feature engineering approach for classifying depression levels using sleep, physical activity, and demographic data from NHANES 2017–2018. A total of 5,068 respondents were included after preprocessing and PHQ-9 label construction. The target variable was divided into three classes: no-to-minimal depression, mild depression, and depression. Twenty raw predictors were transformed into 15 engineered features representing sleep patterns, sleep-related problems, physical activity, sedentary behavior, and interactions with age and income. Logistic Regression with class_weight = balanced was evaluated using stratified 5-fold cross-validation and compared with several baseline classifiers. The Final 15 FE Only scenario achieved an accuracy of 0.6215 ± 0.0091, macro F1-score of 0.4501 ± 0.0104, balanced accuracy of 0.5146 ± 0.0179, and depression-class recall of 0.6122 ± 0.0622. Compared with Raw Features, depression-class recall increased from 0.5360 ± 0.0541 to 0.6122 ± 0.0622, although the improvement was not statistically significant. These findings indicate that compact cross-domain features can improve sensitivity toward the depression class in an interpretable Logistic Regression setting, but overall predictive gains remain modest. The proposed model is more suitable for exploratory and population-level screening support rather than a stand-alone clinical diagnosis
Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction Rahmayanti Rahmayanti; Rudy Herteno; Setyo Wahyu Saputro; Triando Hamonangan Saragih; Friska Abadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.294

Abstract

Software defect prediction (SDP) is essential for improving software reliability by enabling the early identification of modules that may contain defects before the release stage. SDP commonly exhibits redundant or non-contributory metrics, underscoring the need for feature selection to derive a more informative subset. To address this problem, the present study investigates and compares the effectiveness of three feature-selection strategies: SelectKBest (SKB), Recursive Feature Elimination (RFE), and the hybrid SKB+RFE, in enhancing the performance of tree-based classifiers on the NASA Metrics Data Program (MDP) data collections. The study utilizes three classification algorithms, namely Random Forest (RF), Extra Trees (ET), and Bagging (Decision Tree), with Area Under the Curve (AUC) serving as the primary metric for assessing model performance. Experimental results reveal that the RFE and Extra Trees combination yields the top performance, producing an average AUC of 0.7855. This is subsequently followed by the SKB+RFE+ET configuration, which achieves an AUC of 0.7809, and SKB+ET at 0.7776. These findings demonstrate that iterative wrapper-based approaches such as RFE can identify more relevant and effective feature subsets than filter or hybrid strategies, with the RFE+Extra Trees configuration yielding the strongest overall predictive performance and wrapper-based methods exhibiting higher stability across heterogeneous datasets. Even without hyperparameter tuning and relying solely on class-weighting rather than explicit resampling techniques, the findings offer empirical insight into the isolated influence of feature selection on predictive performance. Overall, the study confirms that RFE combined with Extra Trees offers the strongest predictive performance on NASA MDP data collections and forms a foundation for developing more adaptive and robust models.
Metaheuristic-Based Hyperparameter Optimization Analysis of Deep Neural Network for Cross-Project Defect Prediction in Mobile Applications Maulana Abdul Rahman; Rudy Herteno; Radityo Adi Nugroho; Friska Abadi; Setyo Wahyu Saputro
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.340

Abstract

Software Defect Prediction (SDP) plays a strategic role in identifying software defects during the early stages of development, thereby enabling more efficient allocation of testing resources, particularly in the rapidly evolving mobile application domain characterized by fast release cycles. The commonly used Within-Project Defect Prediction (WPDP) approach is often constrained by the limited availability of historical data, especially in projects at early stages of development. As an alternative, Cross-Project Defect Prediction (CPDP) leverages historical data from other projects as training sources. Moreover, the performance of the Deep Neural Network (DNN) used in SDP is highly dependent on accurate hyperparameter configurations, where manual tuning requires substantial time and computational resources without guaranteeing optimal results. To address this issue, this study analyzes and compares the effectiveness of three metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), in optimizing DNN hyperparameters within a CPDP framework. This study utilizes 14 open-source Android mobile application projects and employs the Leave-One-Out Cross-Validation technique. The performance of each combination is evaluated using ROC-AUC as the primary metric. The Wilcoxon Signed-Rank Test with a Bonferroni correction is used to assess the statistical significance of the observed performance differences. The experimental results demonstrate that GWO-DNN achieves the best performance, with an average ROC-AUC of 0.721, and is the only combination that remains statistically significant after Bonferroni correction, with a small effect size based on Cliff’s delta. Overall, the findings of this study indicate that metaheuristic-based hyperparameter tuning is a sufficiently effective approach for improving the capability of DNN in cross-project software defect prediction within the mobile application domain, although the observed improvements remain moderate.
Co-Authors Dodon Turianto Nugrahadi Fatma Indriani Friska Abadi Maulana Abdul Rahman Nila Yoga Tama Nurwati Radityo Adi Nugroho Rahmayanti Rahmayanti Setyo Wahyu Saputro Triando Hamonangan Saragih