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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi 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 Informatika dan Rekayasa Perangkat Lunak Jurnal Fisika Indonesia 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.
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.
Comparative Analysis of Edge Detection Method on Cardboard Packaging Images Susanti, Nanik; Widodo, Catur Edi; Setiawan, Arif
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

Edge detection is a crucial process in digital image processing, particularly in automated visual inspection systems for packaging quality control. Cardboard packaging used in traditional food products often experiences deformation due to mechanical stress or poor distribution, thus requiring a reliable damage detection method. This study aims to compare the performance of five classical edge detection algorithms, Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LoG), in identifying contours and structural deformations in product packaging images. Data were obtained through the acquisition of five cardboard images using a high-resolution smartphone camera. The processing steps include image conversion to grayscale, application of the edge detection algorithm, and quantitative evaluation of the results. The evaluation was conducted using three main metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and processing time. The results show that the Sobel algorithm provides the best performance, with the highest PSNR and lowest MSE values consistently, despite having the longest processing time. In contrast, the Canny algorithm shows the highest efficiency in speed, but with low detection quality. Prewitt and LoG yielded relatively balanced intermediate results between accuracy and efficiency, while Roberts performed moderately across all aspects. These findings indicate that algorithm selection should be tailored to system requirements. Sobel is more appropriate for applications that prioritise accuracy, while Canny is recommended for real-time systems. This study provides an initial basis for the development of lightweight visual inspection systems in the traditional food industry and the MSME sector
Comparative Approaches to Clustering for Profiling Students in Educational Data Mining Azizah, Noor; Kusworo Adi; Catur Edi Widodo
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

This study aims to compare the performance of five clustering algorithms, a K-Means, K-Medoids, Fuzzy C-Means (FCM), DBSCAN, and Gaussian Mixture Model (GMM) in profiling 239 students using quantitative data. The methodology includes data collection, refinement, transformation, application of clustering algorithms, and evaluation using the Silhouette Score, Davies–Bouldin Index, and execution time. The results indicate that K-Means provides the most balanced performance, achieving the highest Silhouette score with well-defined cluster separation. K-Medoids and GMM demonstrate competitive performance, while DBSCAN excels in detecting outliers but produces an excessive number of clusters, limiting its interpretability for profiling. FCM performs the weakest due to poor cluster separability. Overall, K-Means is recommended as the primary approach for student profiling, while other algorithms may complement specific analytical needs.
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 eka rahmawati Eko Sediyono ellya helmud, ellya 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 Nanik Susanti Noor Azizah Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu 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 widya astuti Zainal Bachrudin