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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Fisika Indonesia Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Jurnal Algoritma Journal of Emerging Science and Engineering Scientific Journal of Informatics
Catur Edi Widodo
Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Machine Learning for Post-Disaster Building Damage Classification and Rehabilitation Recommendation: A Review Rahmawati, Eka; Widodo, Catur Edi; Koesuma, Sorja
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2532

Abstract

Accurate classification of building damage following disasters plays a critical role in facilitating efficient rehabilitation and reconstruction. Traditional field-based assessment methods, however, present significant limitations—including time inefficiencies, susceptibility to subjective interpretation, and potential safety risks for survey personnel. Recent advancements in machine learning (ML) have significantly improved the efficiency and objectivity of post-disaster damage assessment by leveraging diverse data sources such as satellite imagery, unmanned aerial vehicles (UAVs), and even crowdsourced social media content. This study conducts a narrative literature review of 78 peer-reviewed articles published from 2020 to 2024, focusing on ML-driven methodologies for classifying building damage and generating rehabilitation recommendations. The literature review reveals a prevailing reliance on deep learning models—especially convolutional neural networks (CNNs) and transformer-based architectures—due to their robust accuracy and adaptability across varied disaster scenarios. Furthermore, novel approaches like self-supervised learning, ensemble methods, and few-shot learning show promising potential in addressing challenges posed by sparse or unevenly distributed datasets. Despite rapid advancements in ML-based post-disaster building damage classification, real-world implementation remains constrained. This review synthesizes current trends, persistent challenges, and critical research gaps to inform the development of a robust ML framework for post-disaster recovery efforts. This study uniquely highlights the integration of ML-based classification with rehabilitation planning frameworks, providing practical guidance for disaster management agencies to optimize post-disaster recovery strategies.
Automatic Detection of Cyberbullying on Text, Image, and Video: A Systematic Literature Review Fitro, Achmad; Wibowo, Mochamad Agung; Widodo, Catur Edi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2542

Abstract

This study presents a systematic literature review (SLR) on the automatic detection of cyberbullying across multiple media modalities, including text, images, and videos, between 2020 and 2025. Unlike previous SLRs that focused only on textual or unimodal data, this research provides a comprehensive synthesis of multimodal approaches that integrate linguistic, visual, and audiovisual cues. Using the PRISMA framework, 4,272 records were screened, resulting in 120 studies for full analysis. The findings reveal a sharp increase in publications in 2025, driven by advances in large language models (LLMs), multimodal transformers, and heightened global attention to online safety. Quantitatively, 69% of studies focused on text-based detection, 21% on multimodal (text-image), and 10% on video-based approaches. NLP, CNN, SVM, BERT, and LSTM remain the most commonly used models, while emerging hybrid frameworks (e.g., ResNet–BiLSTM) show promising performance. Previous studies were often limited by real-time detection capabilities, fairness concerns, and lack of explainable AI. This SLR addresses those gaps by synthesizing methodological trends, highlighting ethical challenges, and identifying opportunities for future integration of explainable and human-centered AI. The practical implication of this study lies in providing a structured reference for researchers, policymakers, and social media platforms to design fair, transparent, and adaptive cyberbullying detection systems.
Integrated Maturity Assessment of Information Security for Land and Building Tax Management System Using National Institute of Standards and Technology Cybersecurity Framework 2.0, International Organization for Standardization/International Electrotechnical Commission 27002:2022, and Cybersecurity Capability Maturity Model 2.1. Paramesvari, Dhenok Prastyaningtyas; Suseno, Jatmiko Endro; Widodo, Catur Edi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5551

Abstract

Regional tax information systems such as the Sistem Informasi Manajemen Objek Pajak (SISMIOP) are vulnerable to cybersecurity threats due to the sensitivity of taxpayer data and the persistence of ad-hoc security management practices. These conditions pose risks to data confidentiality, integrity, and service availability, potentially undermining public trust and the effectiveness of local government services. This study aims to assess the information security maturity of SISMIOP operated by the Badan Pengelolaan Pendapatan, Keuangan, dan Aset Daerah (BPPKAD) through an integrated application of the NIST Cybersecurity Framework (CSF) 2.0, ISO/IEC 27002:2022, and the Cybersecurity Capability Maturity Model (C2M2) 2.1. A qualitative case study approach was employed. An organizational profile was developed using interviews, observations, and document analysis, followed by mapping 38 relevant NIST CSF subcategories to ISO/IEC 27002 controls and C2M2 capability domains. Security maturity was evaluated using questionnaires and interviews based on the C2M2 Maturity Indicator Levels (MIL0-MIL3), and a gap analysis was conducted against the target maturity level of MIL2. The results show that most cybersecurity functions, Govern, Identify, Detect, Respond, and Recover, remain at MIL1, indicating that practices are performed but not yet formalized or consistently implemented. The Protect function partially achieved MIL2. The largest gaps were identified in governance and risk management domains. Based on these findings, 38 prioritized strategic recommendations were formulated to improve policy formalization, risk management, technical controls, monitoring, and incident handling. This study contributes a practical and replicable multi-framework maturity assessment model to strengthen information security governance in public-sector tax information systems.
CT Radiomics and Ensemble Learning for 5-Year Survival Prediction in Colorectal Liver Metastases Astuti, Widya; Widodo, Catur Edi; Soesanto, Qidir Maulana Binu
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.39071

Abstract

Colorectal liver metastases (CRLM) significantly impact patient survival with high recurrence rates. Traditional prognostic models often overlook tumor heterogeneity, leading to suboptimal risk stratification. To address this, radiomics was employed to quantify sub-visual tumor phenotypes, while ensemble learning was selected to robustly handle high-dimensional feature complexity and improve generalization capability. This retrospective study analyzed 145 CRLM patients from The Cancer Imaging Archive, extracting 1130 radiomics features from preoperative CT scans alongside clinical variables. Data were split into training (n=101) and testing (n=44) sets, with feature selection reducing the input to 16 key features. Three ensemble models (XGBoost, LightGBM, Random Forest) were optimized using Optuna, incorporating SMOTE and isotonic calibration. On the test set, XGBoost achieved ROC-AUC 0.918, sensitivity 0.739, and specificity 0.952. LightGBM yielded ROC-AUC 0.916, sensitivity 0.782, and specificity 0.904. Random Forest recorded ROC-AUC 0.888, sensitivity 0.826, and specificity 0.667. Key features included "progression or recurrence" and wavelet-based texture metrics reflecting tumor heterogeneity. These findings demonstrate the effectiveness of combining CT radiomics with gradient boosting models to capture complex prognostic patterns. This integration enhances 5-year survival prediction in CRLM, offering a non-invasive tool for personalized risk stratification and improved clinical decision-making compared to the currently utilized traditional prognostic models.
Optimizing Monkeypox Detection Using Advanced Class Imbalance Handling Methods: Smote, Smote-Enn, Smote-Tomek, Borderline-Smote Rizki, Fahlul; Widowati, Widowati; Widodo, Catur Edi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1286

Abstract

Monkeypox is a zoonotic viral disease with increasing global concern due to its rapid spread and potential public health impact. Accurate and timely detection is crucial, yet the development of machine learning-based detection systems is often challenged by class imbalance in clinical datasets, leading to biased predictions towards majority classes. This study systematically evaluates the effectiveness of various class imbalance handling techniques, including SMOTE, Borderline-SMOTE, SMOTE-ENN, and SMOTE-Tomek, on the performance of ensemble learning algorithms, specifically Random Forest and Gradient Boosting, for monkeypox detection. Using a dataset of 25,000 synthetic patient records with 11 clinical features, models were trained and validated through stratified 5-fold cross-validation. Performance metrics including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), along with ROC analysis, were employed to assess the impact of each augmentation method. Results indicate that hybrid methods, particularly SMOTE-ENN, significantly improve recall and F1-score, improving the detection of clinically important monkeypox-positive cases while maintaining adequate discriminative ability. Standard SMOTE and SMOTE-Tomek provide stable performance across metrics, whereas Borderline-SMOTE shows lower recall despite high precision. These findings highlight the importance of selecting appropriate class imbalance handling strategies tailored to the clinical objective, emphasizing sensitivity in detecting positive monkeypox cases. The study provides practical guidance for implementing reliable and robust machine learning models in early monkeypox detection, contributing to improved clinical decision-making and public health interventions.
Perbandingan Model Machine Learning berbasis Badir Framework untuk Deteksi Fraud dan Prediksi Keterlambatan Pengiriman pada Supply Chain Eriya, Eriya; Widodo, Catur Edi; Nugraheni, Dinar Mutiara Kusumo
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rantai pasokan rentan terhadap berbagai risiko operasional, khususnya fraud dan keterlambatan pengiriman, yang dapat mengganggu efisiensi logistik, kinerja perusahaan, dan kepuasan pelanggan. Big Data Analytics dan Machine learning menawarkan pendekatan prediktif yang efektif untuk mengidentifikasi risiko tersebut secara dini, namun kinerja setiap model berbeda sehingga diperlukan evaluasi komparatif untuk menentukan algoritma paling optimal. Penelitian ini bertujuan untuk membandingkan kinerja berbagai model klasifikasi machine learning dalam mendeteksi fraud dan memprediksi keterlambatan pengiriman pada supply chain dengan menerapkan BADIR Framework sebagai pendekatan analitik terstruktur. Dataset publik DataCo Global digunakan sebagai sampel dengan tahap analisis meliputi identifikasi business question, perencanaan analisis, pengumpulan dan pembersihan data, visualisasi data, insight, dan rekomendasi. Sembilan algoritma klasifikasi diuji, termasuk Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Random Forest, XGBoost, dan Decision Tree. Hasil menunjukkan bahwa Decision Tree merupakan model dengan kinerja terbaik, dengan nilai F1 sebesar 80,35% untuk deteksi fraud dan 99,41% untuk prediksi keterlambatan pengiriman. Temuan ini menegaskan bahwa integrasi BADIR Framework dan machine learning mampu mendukung pengambilan keputusan berbasis data dalam mitigasi risiko supply chain, terutama dalam pengawasan transaksi dan optimalisasi ketepatan pengiriman barang.
Co-Authors Agusta Rakhmat Taufani Agustiyar Agustiyar Aprilia Ayu Andarinny Arif Setiawan Aris Puji Widodo Aryasa, Jiyestha Aji Dharma Budi Warsito Choirul Anam AM Diponegoro Dinar Mutiara Kusumo Nugraheni eka rahmawati Eko Sediyono ellya helmud, ellya Eriya, Eriya Faisal Akbar Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh Fitro, Achmad H Hadiyanto Hadiyanto Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Idrus, Nurul Asyikin Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Koesuma, Sorja Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Maratullatifah, Yulaikha Muchammad Azam, Muchammad Mustafid Mustafid Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri Paramesvari, Dhenok Prastyaningtyas R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizki, Fahlul Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Siswahyudianto Soesanto, Qidir Maulana Binu Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Wibowo, Mochamad Agung widowati widowati widya astuti Zainal Bachrudin