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Jonson Manurung
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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2025): July, Journal of Defense Technology and Engineering" : 5 Documents clear
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. 
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.
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.

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