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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Applied Information System and Management Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Setyo Wahyu Saputro
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology

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Classification of Eyewitness Social Media Messages for Natural Disaster Monitoring using BERT Variants Hanafi, Muhammad Bashir; Faisal, Mohammad Reza; Abadi, Friska; Budiman, Irwan; Saputro, Setyo Wahyu; Mbeledogu, Njideka Nkemdilim
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5317

Abstract

The rapid growth of disaster-related social media data demands effective monitoring. However, its real-time source presents challenges due to large volumes of unstructured and noisy data. This study aims to improve effective monitoring with BERT variants to classify eyewitness reports on Twitter/X. Earlier studies have applied machine-learning and deep-learning models to automate the monitoring of eyewitness messages on social media, but these models still have shortcomings. Traditional machine-learning models rely on handcrafted and frequency-based features, limiting their ability to capture contextual semantics. Deep-learning models offer improved performance but still face challenges in modeling long-range dependencies and handling high-volume social media streams. This issue is pronounced in social media streams. This study employs transformer-based models using several BERT variants (BERT, RoBERTa, DistilBERT, ELECTRA, and ALBERT). Each model is pre-trained with the Masked Language Modeling (MLM) objective, and batch-size optimization is applied to boost performance. Experimental results indicate that a batch size of 16 consistently yields the best performance, with the standard BERT model achieving the highest macro-F1 score of 0.762. By disaster type, macro-F1 scores reach 0.744 for hurricane, 0.793 for flood, 0.756 for earthquake, and 0.750 for wildfire. BERT (16) outperforms the other BERT variants and twelve baseline models from prior research. Unlike previous approaches, this study leverages pre-trained Masked Language Models to optimize classification on disaster-related datasets. The findings contribute to the development of transformer-based architectures for text classification in real-time disaster informatics, leading to more accurate situational awareness and reduced delays in emergency decision-making.
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.
Comparative Evaluation of TabKANet with Oversampling and Feature Selection Ablation for Software Defect Prediction Muhammad Faza Azhiman Saputra; Setyo Wahyu Saputro; Mohammad Reza Faisal; Radityo Adi Nugroho; Andi Farmadi
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.v8i3.351

Abstract

Software defect prediction (SDP) focuses limited testing resources on the modules most likely to fail, but real-world software metric data are tabular, noisy, and severely class-imbalanced, which degrades conventional learners. The Kolmogorov-Arnold Network (KAN) and Transformer architectures recently achieved strong results on tabular data, yet their combined form, TabKANet, has not been evaluated for SDP, nor has the contribution of common preprocessing techniques been quantified. This study adapts and comparatively evaluates TabKANet against established baselines and measures the contribution of oversampling and feature selection through a structured ablation. Twelve all-numerical NASA Metrics Data Program datasets were used. The pipeline applied duplicate removal, MinMax normalization, effective class weighting, and stratified five-fold cross-validation, with oversampling (SMOTE) and Recursive Feature Elimination (RFE) inserted inside the training folds. Four TabKANet variants (A: base, B: +SMOTE, C: +RFE, D: +SMOTE+RFE) were compared with Multi-Layer Perceptron (MLP), standalone KAN, and TabNet, and differences were tested with the Wilcoxon signed-rank test at a 0.05 significance level. The base TabKANet (variant A) achieved the highest mean AUC of 0.7603, slightly ahead of MLP (0.7594) and KAN (0.7583) and well above TabNet (0.7092). Its advantage over TabNet was significant (p = 0.002), whereas it was statistically equivalent to MLP and KAN (p = 0.733). TabNet attained the highest recall (0.739) but the lowest precision (0.228), indicating over-prediction of defects, while TabKANet kept precision and recall balanced. In the ablation, SMOTE significantly reduced AUC (p = 0.042), RFE caused no significant change (p = 0.733), and their combination stayed neutral (p = 0.266). TabKANet therefore performed best without additional resampling. TabKANet is thus a competitive architecture for all-numerical, highly imbalanced SDP, matching strong neural baselines and surpassing TabNet, where effective class weighting alone suffices and SMOTE is counter-productive.
Development of an ORMAWA Work Program Management Information System Using a User-Centered Design Approach Muhammad Helmi; Mohammad Reza Faisal; Friska Abadi; Dodon Turianto Nugrahadi; Setyo Wahyu Saputro; Deni Sutaji
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.46734

Abstract

Student organizations (ORMAWA) at the Faculty of Mathematics and Natural Sciences, Universitas Lambung Mangkurat, face significant challenges in managing their work programs manually. This reliance on paper-based processes and decentralized record-keeping leads to chronic delays, documentation errors, and difficulties in tracking accountability. These issues severely hamper efficient coordination with faculty administrators, complicating timely decision-making and budget monitoring. The primary aim of this research is to develop and implement a web-based work program management information system for ORMAWA using a User-Centered Design (UCD) approach. Specific objectives include providing a centralized digital platform for program submission, approval, and reporting, and significantly enhancing the administrative efficiency and accountability of the entire workflow. The research methodology involved requirements gathering, iterative system design, and implementation using the React JS and Laravel frameworks. Evaluation was conducted through black box testing and User Acceptance Testing (UAT) with 13 ORMAWA administrators. Results demonstrate high user satisfaction (85%) and a substantial 30% efficiency improvement in program submission and reporting processes. The UCD approach was crucial in delivering a system that successfully eliminated redundant administrative tasks and centralized documentation. This study contributes to the application of UCD in developing organizational management systems in higher education, demonstrating how technology can transform traditional administrative workflow.
Co-Authors Akhmad Zakirin Akhtar, Zarif Bin Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal Badali, Rahmat Amin Deni Sutaji Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Elvina Nur Hana Fadhillah, Muhammad Alif Fatma Indriani Fatma Indriani Febrian, Muhamad Michael Friska Abadi Friska Abadi Hanafi, Muhammad Bashir Hariyady Hariyady Herteno, Rudy Hertono, Rudy Irwan Budiman Irwan Budiman Kamil, Hawariul Kenji Satou Khusnul Rahmi Maulidha Martalisa, Asri Maulana Abdul Rahman Mbeledogu, Njideka Nkemdilim Mohammad Reza Faisal Muhammad Faza Azhiman Saputra Muhammad Fikri Muhammad Helmi Muhammad Ikhwanul Hakim Muhammad Itqan Mazdadi Muhammad Khalid Maulana Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Aulia Ramadhan Muliadi Nabella, Putri Nur Hidayatullah, Wildan Puput Dani Prasetyo Adi Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahmat Budianoor Rahmayanti Rahmayanti Rahmina Ulfah Aflaha Raidra Zeniananto Ramadhan, As'ary Reza Faisal, Mohammad Rezeki, Abdillah Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Saragih, Triando Hamonangan Suryadi, Mulia Kevin Syahputra, Muhammad Reza Syarif Maulana, Syarif Triando Hamonangan Saragih Ujang Ruyandi Wahyudi Ariannor Wardana, Muhammad Difha YILDIZ, Oktay Yulia Yudihartanti