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All Journal Journal of Defense Technology and Engineering
Nick Holson M. Silalahi
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Adaptive ant colony optimization integrated with dynamic risk mapping for tactical vehicle path planning in dynamic battlefields Nick Holson M. Silalahi; Eryan Ahmad Firdaus; Herwin Melyanus Hutapea
Journal of Defense Technology and Engineering Vol. 1 No. 1 (2025): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The movement of combat vehicles in modern battlefields faces complex challenges in the form of uncertain terrain, dynamic enemy threats, and limited real-time information, making conventional methods such as Dijkstra or A* less capable of optimising routes adaptively. This research aims to develop an Adaptive Ant Colony Optimization (ACO) algorithm model integrated with a dynamic risk map to determine safe, fast, and efficient routes for combat vehicles. The methodology employed includes designing an adaptive ACO with risk-based pheromone update mechanisms, modeling dynamic risk maps using Gaussian probability functions and Markov models, and conducting graph-based battlefield simulations to evaluate algorithm performance. Evaluation was conducted by comparing the adaptive ACO with baseline algorithms (Dijkstra, A*, and Particle Swarm Optimization) using metrics such as Safety Index (SI), Time Efficiency (TE), Adaptability, and Computational Cost (CC). The results show that the adaptive ACO consistently produces paths with the highest SI values, competitive time efficiency, and better real-time adaptability compared to the baseline, while path visualization demonstrates the algorithm's ability to dynamically avoid high-risk areas. These findings indicate that integrating adaptive ACO with dynamic risk maps provides safer and more flexible navigation strategies, with significant potential for application in autonomous combat vehicles, UAV systems, and military operations based on intelligent simulation. This research contributes to the development of adaptive path optimization algorithms in dynamic battlefields, bridges the literature gap related to risk-based navigation, and provides a framework that can serve as the foundation for developing military decision support systems based on artificial intelligence. 
Multimodal deep learning framework for detection and attribution of adversarial information operations on social media platforms Nick Holson M. Silalahi; Jonson Manurung; Bagus Hendra Saputra
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Adversarial information operations on social media platforms pose critical threats to national security, with state-sponsored actors exploiting multimodal content manipulation to conduct sophisticated disinformation campaigns. Existing detection approaches focus on single-modality analysis, lacking comprehensive frameworks for simultaneous detection, attribution, and coordination identification. This research develops an integrated multimodal deep learning framework combining RoBERTa-large transformer, Vision Transformer, Graph Convolutional Networks, and bidirectional LSTM, unified through cross-modal attention fusion with multi-task learning optimization. Experimental validation utilizes eight datasets including Russian IRA tweets (3.8M posts), Fakeddit (1M submissions), TweepFake (25K accounts), FakeNewsNet (23K articles), MM-COVID (6.7K posts), CREDBANK (60M tweets), and MEMES (12K items). Results demonstrate 93.24% detection accuracy, 79.34% attribution accuracy across 15 threat actor groups, 91.67% coordination F1-score, 88.62% narrative classification accuracy, and 448ms inference latency suitable for real-time deployment. Ablation studies reveal graph neural networks provide largest performance contribution (5.82% improvement), highlighting social network analysis importance for detecting coordinated behavior. Future directions include large-scale pre-training, adversarial training, continual learning, human-AI collaboration, multilingual expansion, federated learning, and causal inference methods.
Co-Authors Bagus Hendra Saputra Eryan Ahmad Firdaus Herwin Melyanus Hutapea Manurung, Jonson