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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 Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics 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.
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 Didik Supriyadi, Didik 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 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 Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Wibowo, Mochamad Agung Zainal Bachrudin