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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 25 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.
Digital propaganda content detection using a transformer-based model on social media platforms Jonson Manurung; Hengki Tamando Sihotang; R. Fanry Siahaan
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Digital propaganda on social media has emerged as a critical challenge to democratic stability and national security. Although transformer-based language models have demonstrated promising performance in text classification, their effectiveness for propaganda detection is often constrained by subtle rhetorical manipulation and severe class imbalance, leading to biased predictions toward majority classes. This study addresses these limitations by proposing a fine-tuned RoBERTa-base model integrated with a class-weighted cross-entropy loss to improve the recognition of minority propaganda instances. The model was trained and evaluated on the SemEval-2020 Task 11 sentence-level corpus containing 14,857 annotated sentences, partitioned into 11,886 training samples and 2,971 test samples using stratified sampling. RoBERTa was selected because its robust pre-training strategy and dynamic masking enable more effective contextual representation learning, while class-weighted loss mitigates the adverse effects of the 22.80% to 77.20% class imbalance by assigning weights of 2.19 and 0.65 to the propaganda and non-propaganda classes, respectively. Under identical fine-tuning settings, the proposed model was compared with BERT-base and DistilBERT-base to ensure a fair architectural evaluation. Experimental results demonstrate that RoBERTa achieved the best performance, attaining 93.47% accuracy and a macro-F1 score of 91.82%, outperforming BERT by 1.28 percentage points and DistilBERT by 2.89 percentage points in macro-F1. These findings demonstrate that combining RoBERTa with class-weighted learning provides a robust and practical approach for propaganda detection, supporting the development of automated content moderation and misinformation monitoring systems for social media platforms. Future work will investigate multilingual propaganda detection and fine-grained propaganda technique classification.
RSA algorithm optimization using a quantum inspired genetic algorithm for defense communication security Eryan Ahmad Firdaus; Jonas Franky Panggabean; Jeremia Paska Sinaga
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

RSA key generation in fifth-generation (5G) defense communication networks faces significant computational challenges due to the time-intensive search for large prime numbers, resulting in increased key generation latency and reduced operational efficiency. Existing optimization approaches based on conventional evolutionary algorithms often suffer from premature convergence and limited exploration of the prime search space. This study proposes a Quantum Inspired Genetic Algorithm (QIGA) that represents prime candidates as quantum chromosomes using probability-amplitude pairs and updates candidate solutions through a quantum rotation-gate mechanism, enabling a more diverse and efficient search than classical genetic algorithm-based RSA optimization. The proposed approach was evaluated through a controlled benchmark comprising 900 experimental observations across three RSA key sizes (1024-bit, 2048-bit, and 4096-bit), with 100 independent trials for each experimental condition, and compared against standard RSA and a classical Genetic Algorithm under identical settings. Performance was assessed using key generation latency, encryption throughput, decryption throughput, and key entropy, as these metrics collectively measure computational efficiency, cryptographic processing capability, and the randomness required for secure key generation. Experimental results demonstrate that the proposed QIGA reduced key generation latency by 40.12% and increased encryption throughput by 65.78% for 2048-bit RSA compared with the standard implementation, while producing high-quality keys with an entropy of 2041.9 bits and achieving population convergence at generation 31. These findings indicate that QIGA provides an effective and practical optimization strategy for accelerating secure RSA key generation while preserving cryptographic strength, making it suitable for low-latency, high-security defense communication systems. Future work will investigate integration with post-quantum cryptographic schemes and hardware acceleration through field-programmable gate arrays (FPGAs) for deployment in resource-constrained tactical environments.
Cyber threat detection on social media using indoBERT and sentiment analysis Bagus Hendra Saputra; Jonson Manurung; Baringin Sianipar; R. Fanry Siahaan
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The rapid growth of Indonesian social media has increased the spread of cyber threat-related content, creating significant challenges for digital security monitoring due to the informal language, code-switching, and sentiment-rich expressions commonly used in online communication. Existing detection approaches, particularly those based on multilingual or English-centric language models, often fail to capture the linguistic characteristics of Indonesian text effectively. This study aims to develop an accurate cyber threat detection model by fine-tuning IndoBERT, a transformer-based language model pretrained on a large-scale Indonesian corpus, for binary Threat and Non-Threat classification. The model was trained and evaluated using the Tweet ID Sentiment Dataset containing 10,800 annotated tweets, which were partitioned into training, validation, and test sets, and its performance was compared with four baseline methods: SVM with TF-IDF features, CNN with FastText embeddings, BiLSTM with Word2Vec representations, and multilingual BERT. Experimental results demonstrate that the proposed IndoBERT model achieved the best performance, obtaining an accuracy of 0.9389, a macro-F1 score of 0.9292, and a Threat-class recall of 0.9486, consistently outperforming all baseline models. The novelty of this study lies in demonstrating the effectiveness of a monolingual Indonesian pretrained transformer for cyber threat detection, highlighting the importance of language-specific contextual representations in improving classification performance. These findings indicate that the proposed approach provides a robust and practical solution for automated cyber threat detection, supporting early warning systems and digital security monitoring in Indonesian social media environments. Future work will investigate multiclass cyber threat categorization and cross-platform evaluation to improve model generalizability in real-world applications.
Vehicle activity classification using vibration sensors and support vector machine for perimeter security systems Nick Holson M. Silalahi; Helmi Kurniawan; Efouri Baulolo
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Automated activity classification is essential for passive military perimeter surveillance, where reliable detection of personnel and vehicle intrusion must be achieved without revealing the presence of monitoring infrastructure. Existing approaches often experience performance degradation under complex vibration patterns and environmental noise, motivating the need for a robust classification method. This study proposes a vibration-based activity classification framework that combines discriminative time- and frequency-domain vibration features with a Support Vector Machine using a Radial Basis Function (SVM-RBF) kernel. The principal novelty of the proposed framework lies in its integration of handcrafted multidomain vibration features with maximum-margin nonlinear classification to enable accurate recognition of multiple intrusion activities from passive seismic signals. SVM-RBF was selected because of its strong generalization capability and effectiveness in handling nonlinear decision boundaries in moderate-sized datasets, making it well suited for vibration-based activity recognition. The proposed model was evaluated on the Perimeter Vibration Accelerometer Dataset containing 2,400 balanced signal segments representing four activity classes: Heavy Vehicle, Light Vehicle, Personnel, and Background. Experimental results demonstrate that the proposed approach achieved an overall accuracy of 0.9250 and a macro-F1 score of 0.9247, outperforming k-nearest neighbor, Random Forest, and Multilayer Perceptron classifiers. These findings indicate that the proposed framework provides a reliable and computationally efficient solution for passive military perimeter surveillance, supporting early intrusion detection while maintaining the covert operation of defense monitoring systems. Future work will investigate multi-sensor fusion, deep learning on raw vibration signals, and embedded deployment for real-time operation in diverse environmental conditions.
Distributed denial of service attack prediction using a hybrid CNN GRU model on defense network infrastructure Muhammad Azhar Prabukusumo; Marthen Doga; Yulianus Kaisiepo
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Distributed Denial of Service (DDoS) attacks pose a serious threat to defense network infrastructure by disrupting communication services that are critical for command-and-control operations. Existing detection methods often struggle to simultaneously capture the spatial characteristics of network traffic and the temporal evolution of attack patterns, limiting their effectiveness against diverse DDoS attacks. This study proposes a hybrid CNN-GRU model that integrates one-dimensional convolutional neural networks for local feature extraction with gated recurrent units for temporal dependency modeling, enabling more comprehensive representation learning than single-model approaches. The proposed framework was evaluated on a five-class subset of the CIC-DDoS2019 dataset containing 225,000 network flow records and compared with standalone CNN, GRU, and LSTM models under identical experimental settings. Experimental results demonstrate that the proposed CNN-GRU achieved the best performance, obtaining an accuracy of 0.9707 and a macro-F1 score of 0.9707, consistently outperforming all baseline models. The novelty of this study lies in the effective integration of complementary spatial and temporal learning mechanisms for multiclass DDoS attack detection, providing a more robust classification framework for defense-oriented network traffic analysis. These findings indicate that the proposed model offers a practical solution for intelligent intrusion detection and early warning systems, supporting resilient and secure defense network operations against evolving DDoS threats. Future work will extend the model to the complete twelve-class attack taxonomy and investigate attention mechanisms to further improve discrimination among closely related attack categories.

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