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Journal of Defense Technology and Engineering
Published by Universitas Pertahanan Republik Indonesia
ISSN : -     EISSN : 31102484     DOI : -
Core Subject : Science, Engineering,
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
Journal of Defense Technology and Engineering is a peer-reviewed, open-access scientific journal dedicated to the advancement of research and development in the fields of defense technology, engineering innovation, and related interdisciplinary studies. Published by the Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia, Journal of Defense Technology and Engineering provides a platform for scholars, researchers, practitioners, and industry professionals to disseminate original research, technical reports, and review articles that address current and emerging challenges in defense and security. The journal welcomes contributions in a wide range of topics including but not limited to: Advanced weapon systems, Cybersecurity and cryptography, Military communication systems, Artificial intelligence in defense, Robotics and autonomous systems, Materials science and defense engineering, Strategic defense technologies, Simulation and modeling in military applications, Mechanical engineering for defense systems (e.g., propulsion, thermal systems, vehicle mechanics), Civil engineering in military infrastructure (e.g., fortification design, military base development, disaster-resistant structures), Electrical engineering in defense technology (e.g., radar systems, electronic warfare, power systems in defense equipment) Journal of Defense Technology and Engineering aims to foster scientific knowledge exchange and technological innovation that support national and international defense strategies. The journal is published biannually and adheres to strict ethical publishing standards to ensure the integrity and quality of each publication. ISSN (Online): [3110-2484] Publishing Frequency: Biannual (July and January) Language: English Publisher: Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 15 Documents
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Big data analytics framework for defense strategic intelligence and decision support systems Rochedi Idul Adha; Adam Mardamsyah; Khaerul Imam Phatoni
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

The contemporary defense environment faces rapidly evolving threats, vast heterogeneous data, and linguistic diversity, creating significant challenges for timely and accurate intelligence analysis. This study aims to develop an integrated big data analytics framework that combines open-source intelligence, social media monitoring, and satellite imagery into a unified temporal knowledge graph to support multilingual, cross-modal threat assessment. The proposed methodology incorporates five key phases: multi-source data collection and preprocessing, multilingual transformer-based natural language processing for entity, relation, and event extraction, temporal knowledge graph construction, machine learning-driven analytical modeling for threat prediction and risk assessment, and comprehensive evaluation using both system performance and intelligence value metrics. Experimental results demonstrate that the framework achieves superior entity recognition (F1-score 0.882) and relation extraction (F1-score 0.869), reduces processing latency by 92.6% compared to baseline systems, and integrates 6.3 million entities across 15 languages. Multi-source data fusion improves assessment accuracy by 18.4%, enabling near real-time situational awareness and enhanced strategic decision-making. The system’s explainable reasoning and temporal modeling capabilities provide transparent, actionable intelligence for defense planners, addressing limitations of traditional single-modality and monolingual systems. These findings indicate that integrating multilingual NLP, cross-modal fusion, and temporal knowledge representation significantly enhances operational readiness and early warning capabilities, offering a practical framework adaptable to national and regional security contexts.
Security threat prediction model using graph neural networks and deep temporal learning Eryan Ahmad Firdaus; Adam Mardamsyah; Jeremia Paskah Sinaga
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

The increasing complexity and interconnectedness of modern security threats, including terrorism, social unrest, and transnational conflicts, pose significant challenges for traditional intelligence and threat detection systems, which struggle to capture both relational and temporal dynamics of evolving security environments. This study aims to develop a predictive framework capable of providing early warnings of emerging security threats by integrating graph-based relational modeling with temporal sequence learning. We propose a hybrid architecture combining Graph Neural Networks (GNN) with bidirectional Long Short-Term Memory (LSTM) networks, enhanced with an attention-based fusion mechanism to jointly model actor interactions and temporal evolution. The framework leverages large-scale event data from GDELT and ACLED spanning 2015–2025, encompassing over 9.8 million events and 14,532 unique actors, and constructs dynamic, attributed security networks to capture multi-dimensional actor relationships. Experimental results demonstrate that the proposed GNN-LSTM model achieves an overall accuracy of 94.3% and an F1-score of 88.3% for critical threat detection, outperforming traditional machine learning baselines and providing early warnings up to nine days in advance. The model also offers interpretability by highlighting influential actors and key relational patterns contributing to threat escalation. These findings suggest that integrating relational and temporal information through hybrid deep learning architectures significantly enhances predictive accuracy and operational utility in security threat assessment, offering a practical tool for proactive decision-making and resource allocation in complex security environments.
Blockchain-enhanced security framework for defense supply chain management: an AI-driven smart contract approach with distributed ledger technology Hondor Saragih; Jonson Manurung; Hengki Tamando Sihotang; I Made Aditya Pradhana Putra
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

Defense supply chains face critical security challenges including counterfeit components, unauthorized access, data tampering, and supply chain attacks that compromise operational integrity and national security. Existing blockchain implementations suffer from limited scalability, inadequate threat detection mechanisms, and insufficient integration with modern AI technologies for real-time security monitoring. This research develops an AI-Enhanced Blockchain Security Framework combining smart contracts with distributed ledger technology specifically designed for defense supply chain management. The framework employs multi-signature authentication, cryptographic verification, and machine learning-based anomaly detection across a three-layer architecture (blockchain layer, security layer, analytics layer). Validation using the DataCo supply chain dataset (180K operations) and Backstabber's knife collection attack patterns (174 documented attacks) demonstrates 94.7% attack detection accuracy, 87.3% reduction in unauthorized access attempts, and 99.2% data integrity verification rate. The system achieved 850 transactions per second (TPS) throughput with 1.8-second average latency and 40% cost reduction compared to traditional centralized systems. Smart contract execution showed 99.96% reliability across 10,000 test scenarios with automated enforcement of security policies. Statistical validation confirmed significant superiority over conventional approaches (p<0.001). Future work includes quantum-resistant cryptography, federated learning for privacy-preserving analytics, cross-chain interoperability, and integration with IoT sensors for real-time supply chain monitoring.
A secure image steganography framework for covert communication using asymmetric encryption and Huffman Compression Aulia Khamas Heikhmakhtiar; Nadiza Lediwara; Sembada Denrineksa Bimorogo
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

This paper presents a secure data-hiding framework that combines asymmetric key cryptography, lossless data compression, and image steganography to enhance the confidentiality and imperceptibility of hidden communications. The proposed method encrypts the secret message using an asymmetric encryption scheme, compresses the resulting ciphertext using Huffman coding, and embeds the compressed data into a digital image using a spatial-domain steganographic technique. This multi-layered approach ensures that both the existence and the content of the secret message are protected. Experimental evaluations were conducted using standard image quality metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), to assess visual imperceptibility, along with performance analysis to evaluate computational overhead. The results demonstrate that the proposed method achieves high image quality with minimal distortion while maintaining strong cryptographic security. The integration of compression effectively reduces embedding payload, further improving steganographic performance. The findings indicate that the proposed framework provides a robust and practical solution for secure and covert data transmission.
A multi-objective Particle Swarm Optimization framework for defense logistics decision-making under dynamic and crisis conditions anindito anindito; Adam Mardamsyah; Jonson Manurung
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

The complexity of decision-making in defense logistics systems has increased significantly due to demands for cost efficiency, distribution speed, and operational resilience in dynamic and crisis conditions. Conventional optimization approaches generally fail to capture these conflicting objectives simultaneously. This study aims to develop and evaluate a multi-objective optimization framework based on Multi-Objective Particle Swarm Optimization (MO-PSO) to support adaptive and performance-based defense logistics decision-making. The proposed method optimizes three main objective functions, namely minimizing operational costs, minimizing distribution time, and maximizing logistics readiness levels, with numerical parameter adjustments designed for the defense environment. Simulation results show that MO-PSO is capable of producing a more convergent and evenly distributed Pareto Front compared to comparison methods such as NSGA-II and standard MOPSO, with a 12.4–18.7% increase in hypervolume and a 21.3% decrease in solution dominance error. These findings indicate that the proposed approach is more effective in simultaneously balancing multi-objective trade-offs. Practically, the research results provide policy implications for defense planners in designing logistics strategies that are more efficient, responsive, and resilient to operational uncertainty.

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