Claim Missing Document
Check
Articles

Found 12 Documents
Search

AI-based cyber patrol system for media sentiment analysis on online news regarding the Indonesian Air Force Setyawan, Muhammad Iqbal; Mardamsyah, Adam; Anindito, Anindito; Budiman, Dwi Cahyo
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.497

Abstract

This study presents the development of an adaptive cyber patrol system designed to assist the Indonesian Air Force in monitoring the rapidly changing dynamics of public information. The system aims to detect issues that may influence strategic perception and operational readiness by automatically tracking online news sources across Indonesia. The integrated framework automates the collection, Analysis, and reporting process ranging from identifying viral phrases and performing sentiment Analysis to generating tactical reports for Download. Artificial intelligence techniques are employed to expand keyword coverage, ensure the timeliness of information, and assess the relevance and coherence of collected content. Evaluation results indicate that the system operates reliably and produces well-structured outputs. Overall, this research offers a modular integration of AI, information Analysis, and automated reporting that can be further developed toward predictive and multi-tenant analytics in the future.
Disinformation propagation modeling in digital information warfare using hybrid GNN and LSTM Manurung, Jonson; Saragih, Hondor; Mardamsyah, Adam; Sinaga, Jeremia Paska
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.345

Abstract

The rapid growth of digital information warfare has enabled the widespread dissemination of disinformation, posing serious challenges for detection systems. However, most existing approaches treat disinformation detection as a static classification problem and fail to consider the network structure and temporal dynamics of information spread. This study proposes a hybrid deep learning model that combines Graph Attention Networks (GAT) and Bidirectional Long Short-Term Memory (BiLSTM) with a cross-attention mechanism to capture both structural and temporal patterns of disinformation propagation.  The proposed model was evaluated using three datasets: the PHEME rumor dataset, a large-scale Twitter and X crisis dataset, and a synthetically generated defense simulation dataset. Experimental results show that the model achieves strong performance, with 92.47% accuracy in classification, 89.63% precision in cascade prediction, 87.91% F1-score in source identification, and a mean absolute error of 0.183 in predicting spread dynamics, outperforming several baseline methods. These findings demonstrate that integrating network-based and temporal modeling can significantly improve disinformation detection performance. Future research will focus on incorporating multimodal data, real-time processing, and cross-platform learning to enhance the robustness of the proposed approach.