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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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Designing an Information System “Online Cashier (OK)” Nadiza Lediwara; Sembada Denrineksa Bimorogo; Aulia Khamas Heikmakhtiar; Alvin Reychan Perdana Putra; Dicky Daniel Simarmata; Muhamad Alroy Rizky Pasha Ponto; Alfian Habib Ahmed; Regifia Ningrum Nur Aulia
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

Micro, Small, and Medium Enterprises (MSME) play a crucial role in Indonesia’s economy, yet many still face obstacles in adopting digital technology, particularly in financial transaction recording, which often leads to inefficiency and errors. This study aims to design and develop an “Online Cashier (OK)” system as a solution for Warung Makan Soto Lamongan Cak Munif, which still relies on conventional transaction methods. The system was developed using the Agile method, emphasizing iterative and adaptive development tailored to user needs. The design process applied system modeling tools, including Use Case, Activity, and Class Diagrams, while system performance was evaluated through Black Box Testing. The results showed that the Online Cashier system achieved an overall design success rate of 98.75% and testing effectiveness of 94%, with features such as transaction recording, inventory management, user access control, and report generation functioning properly. The system significantly improves transaction accuracy, reduces operational inefficiency, enhances financial data transparency, and strengthens business management control. Furthermore, the Online Cashier (OK) system provides an opportunity for MSME owners to become more familiar with digital business management, supporting the broader agenda of digital transformation in Indonesia. This study implies that the implementation of web-based cashier systems can enhance MSME competitiveness by enabling structured, efficient, and data-driven decision-making. 
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. 
Comparative study of the bearing capacity of square and rectangular shallow foundations based on Terzaghi and Meyerhof methods Sayed Ahmad Fauzan; Ricky Harianja; Ekodjati Tunggulgeni; Okri Asfino Putra; Muhammad Hamzah Fansuri; Pungky Dharma Saputra; Suprayogi Suprayogi; Fadhil Muhammad Nuryanto
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

Foundation integrity is paramount in evaluating building reliability. This research investigates Building X, a two-story structure with shallow footing foundations on soft soil in East Jakarta. The study compares bearing capacity using Terzaghi and Meyerhof methods, with structural loads modeled in ETABS. Results show Terzaghi’s ultimate bearing capacity (qu) is 15076.00 kg/m2, while Meyerhof’s is 22017.68 kg/m2. Consequently, Terzaghi provides a more conservative Safety Factor (SF). The technical implications are critical for decisions: Terzaghi’s results serve as a lower-bound safety limit to prevent catastrophic shear failure, while Meyerhof offers comprehensive geometry-based parameters. Many points exhibit an SF below the 3.00 standard, with several below 1.00 under Terzaghi analysis. This study contributes to building audits by bridging classical theories with practical Building Functionality Certificate (SLF) requirements in soft soil regions. The findings underscore the necessity for future numerical validation using Plaxis 2D to account for complex non-linear soil-structure interactions and precise settlement behavior.
Particle Swarm Optimization for Multi Objective Optimization of Intrusion Detection in National Defense Cyber Infrastructure Muhammad Azhar Prabukusumo; Jontinus Manullang; Baringin Sianipar
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

Cybersecurity is a critical component of national defense, yet conventional Intrusion Detection Systems (IDS) often face limitations such as high false positive rates, detection delays, and difficulty adapting to dynamic attack patterns, leading to potential blind spots in defense networks. This study aims to design an adaptive IDS that balances detection accuracy, false positives, and operational efficiency through the application of multi objective Particle Swarm Optimization (PSO). Using the CICIDS2017 dataset, which simulates realistic modern network traffic and attack scenarios, we developed and evaluated a PSO optimized IDS model. The experimental methodology included preprocessing, feature selection, model training, and optimization of key performance objectives—maximizing detection rate (DR), minimizing false positive rate (FPR), and reducing latency. The results demonstrate that the proposed PSO IDS achieved a detection rate of 0.96 compared to 0.85 in conventional IDS, reduced the false positive rate from 0.18 to 0.07, and lowered average detection latency from 0.35 seconds to 0.12 seconds. Pareto front analysis confirmed that the multi objective optimization effectively balances conflicting parameters, delivering more robust and resilient intrusion detection. These findings indicate that PSO based multi objective IDS can serve as a practical and scalable solution for strengthening national cyber defense infrastructures, while also providing policy relevant insights on the integration of AI driven optimization methods into defense strategies.
The effect of increased regiment activities on the health condition of cadets Nadiza Lediwara; Sembada Denrineksa Bimorogo; Aulia Khamas Heikmakhtiar; Azzam Amar Ma’ruf; Daffa Mahdy Brata
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 study aims to analyze the effect of increased regiment activities on the health conditions of student cadets at the Republic of Indonesia Defense University (UNHAN RI). Regiment activities, which include physical training, discipline building, and mental development, are considered to influence both physical and mental health aspects of cadets. This study employed a quantitative comparative design using a paired sample approach to examine changes in health conditions before and after an increase in regiment activity intensity. Data were collected using a structured questionnaire and analyzed using a paired sample t-test. The results indicate a statistically significant difference in cadets’ health conditions before and after the increase in regiment activity intensity, as indicated by a p-value of 1.029 × 10⁻⁶ (p < 0.05). These findings provide empirical evidence of the impact of regiment activity intensity on cadet health and highlight the importance of managing training intensity to support optimal health outcomes in military-based educational institutions.
Predicting Interprovincial Rice Food Security in Indonesia as a Pillar of National Defense Using the Random Forest Regressor Algorithm Bagus Hendra Saputra; Ahmad Eryan Firdaus
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

This study investigates interprovincial rice food security in Indonesia as a strategic pillar of national defense. Using a quantitative predictive approach, the Random Forest Regressor algorithm was applied to multidimensional data from all provinces, incorporating variables such as rice expenditure per capita, rice price, production, population, consumption, and harvested area. The results show significant disparities between provinces: surplus regions such as East Java, Lampung, and South Sulawesi contrast sharply with deficit areas like Jakarta, Papua, and Bangka Belitung. Feature importance analysis reveals that supply-side factors, particularly harvested area (50.5%) and production (33.2%), are the most decisive, while demand-side factors have weaker influence. Model evaluation achieved an R² of 0.8239, confirming strong predictive reliability. These findings underscore that rice food security is not only an economic and social issue but also a critical aspect of non-military defense. Strengthening predictive systems and interprovincial distribution networks is essential to ensure resilience against disruptions from disasters, conflicts, or geopolitical instability. The study highlights the practical value of machine learning models in guiding evidence-based policy to secure national food sovereignty.
Recurrent neural network for adaptive cyber attack prediction on critical defense systems Jonson Manurung; Hengki Tamando Sihotang
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 threat of cyber attacks against critical defense systems is becoming increasingly complex and dynamic, requiring adaptive and proactive prediction mechanisms. This study aims to develop a Recurrent Neural Network (RNN) model to predict cyber attacks on critical defense systems with high accuracy and generalization capabilities against new attacks. The CICIDS2020 dataset was used to train and test the model, with 70% of the data allocated for training, 15% for validation, and 15% for testing. The RNN architecture was optimized by selecting the number of hidden layers, the number of neurons per layer, the activation function, and the application of dropout and regularization to minimize the risk of overfitting. The model was trained using the Backpropagation Through Time (BPTT) algorithm and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that RNN outperforms LSTM, Random Forest, and SVM algorithms, with an accuracy of 97.8%, precision of 96.5%, recall of 95.9%, F1-score of 96.2%, and AUC of 0.981, and is capable of detecting rare attacks. These findings confirm the effectiveness of RNN in capturing long-term temporal patterns in cyberattack data and providing adaptive predictions for new attacks. The practical implications of this research include strengthening critical defense systems through early detection and real-time mitigation of cyberattacks, as well as providing a basis for the development of reliable proactive security systems.
Enhanced cyber attack detection using optimized random forest with SMOTE-based class balancing and feature selection Jonson Manurung; Adam Mardamsyah; Baringin Sianipar
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 rapid expansion of interconnected enterprise networks has intensified cybersecurity threats, while traditional signature-based intrusion detection systems remain ineffective against evolving and imbalanced attack patterns, particularly zero-day and low-frequency attacks. This study aims to develop an optimized and practically deployable intrusion detection framework by leveraging a Random Forest classifier on the CIC-IDS2017 benchmark dataset, with emphasis on robust minority attack detection, computational efficiency, and interpretability for real-world security operations. The proposed method integrates comprehensive data preprocessing, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance mitigation, feature importance–driven dimensionality reduction, and exhaustive grid search–based hyperparameter optimization within a unified machine learning pipeline. Experiments conducted on 2.52 million network flow records demonstrate that the optimized model achieves 98.14% accuracy, 96.25% weighted F1-score, and 0.993 ROC-AUC, while maintaining stable performance across all attack categories, including minority classes such as Infiltration and Botnet with F1-scores exceeding 93%. Feature selection reduced dimensionality by 58.3% and training time by 63.2% without degrading performance, enhancing deployment feasibility in enterprise intrusion detection environments. Comparative analysis confirms that the proposed approach outperforms baseline Random Forest models, traditional machine learning methods, and recent deep learning approaches while requiring significantly lower computational resources. These findings indicate that a holistically optimized Random Forest framework offers a reliable, interpretable, and operationally efficient solution for real-world network security monitoring and cyber defense systems.
Cryptographic algorithm optimization for defense data security using quantum inspired algorithms Bagus Hendra Saputra; Jonson Manurung; 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 rapid advancement of quantum computing poses a critical threat to classical public-key cryptographic systems widely used in defense communication infrastructures, while the practical deployment of post-quantum cryptography (PQC) remains constrained by excessive key sizes, computational overhead, and energy consumption in bandwidth- and latency-sensitive military environments. This study aims to develop and evaluate a quantum-inspired multi-objective optimization framework to enhance the operational feasibility of standardized PQC schemes without compromising cryptographic security. The proposed method applies a Quantum Genetic Algorithm (QGA) to optimize configuration parameters of CRYSTALS-Kyber and CRYSTALS-Dilithium by simultaneously balancing security strength, computational performance, resource efficiency, and deployability. Experiments were conducted using official NIST test vectors and defense-oriented communication scenarios, with performance evaluated across encryption and signature latency, throughput, key and signature sizes, memory footprint, and energy consumption, while security was validated against classical and quantum attack models. The results demonstrate that the optimized configurations achieve key and signature size reductions of up to 10.3%, throughput improvements of up to 15.5%, and energy consumption reductions of up to 12.5% compared to baseline NIST implementations, while fully maintaining NIST security levels and robust resistance to quantum adversaries. These improvements significantly enhance the suitability of PQC for tactical radios, satellite communications, and resource-constrained defense platforms. The findings indicate that quantum-inspired multi-objective optimization is a critical enabler for transitioning post-quantum cryptography from theoretical security constructs to deployable, mission-ready solutions in real-world defense systems.
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.

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