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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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LightGBM based malware Classification for Cyber Defense Infrastructure Using the EMBER Portable Executable Feature Dataset Hondor Saragih; Bartolomeu dos Reis; Vicente Soares
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

Malware attacks against defense information systems continue to evolve in complexity, requiring automated multiclass classification methods capable of accurately distinguishing diverse malware families from high-dimensional Portable Executable (PE) features. This study presents a comprehensive evaluation of the LightGBM gradient boosting algorithm for nine-class malware classification using a stratified balanced subset of 405,000 samples from the EMBER benchmark dataset. The novelty of this work lies in providing a standardized comparative evaluation of LightGBM against XGBoost and Random Forest under identical data preprocessing, partitioning, and experimental conditions, enabling a fair assessment of the architectural advantages of each ensemble learning method. After removing zero-variance features, the dataset was represented by 2,350 static PE attributes and divided into training, validation, and testing subsets using a stratified 70/15/15 split. Experimental results demonstrate that the proposed LightGBM model achieved an overall accuracy of 98.12% and a macro F1-score of 98.12%, outperforming XGBoost and Random Forest under the same evaluation protocol. These findings indicate that LightGBM effectively captures complex feature interactions while maintaining computational efficiency for large-scale malware classification. The study contributes a reproducible benchmarking framework for multiclass malware classification and provides empirical evidence supporting the adoption of LightGBM as a practical ensemble learning approach for cyber defense applications. Future work will investigate the integration of dynamic behavioral features, explainable artificial intelligence techniques, and temporal malware datasets to improve model robustness against concept drift and adversarial attacks.
Feature selection optimization for heart disease classification using particle swarm optimization and random forest Adelard Paul Kimario; Godfrey Sebastian Mgasa; Abduel Boniface Sechuma
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

Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accurate and efficient computer-aided diagnostic methods. Although machine learning has shown considerable potential for heart disease prediction, the presence of redundant and irrelevant clinical features often limits classification performance and model interpretability. This study proposes a wrapper-based feature selection framework that integrates Particle Swarm Optimization (PSO) with the Random Forest (RF) classifier for heart disease classification using the Heart Disease UCI dataset. The proposed framework applies data preprocessing, including missing value imputation, categorical encoding, feature scaling, and binary target transformation, followed by PSO-based feature selection and Random Forest classification. Experimental results demonstrate that the proposed PSO-RF framework reduces feature dimensionality while improving predictive performance compared with the baseline Random Forest model. The optimized model achieved an accuracy of 88.59% with an AUC of 0.9279, indicating its effectiveness in identifying the most informative clinical attributes and enhancing classification capability. The novelty of this study lies in the systematic integration of wrapper-based PSO feature selection with Random Forest under a unified evaluation framework, providing an effective balance between predictive accuracy and model simplicity. These findings demonstrate the potential of the proposed approach to support intelligent clinical decision-support systems by improving diagnostic reliability while reducing computational complexity in cardiovascular disease prediction.
Optimizing customer churn prediction using hybrid genetic algorithm and extreme gradient boosting Simba Daniel Samuel; Kamelo Gabriel Jose; Jabari Manuel Carlos; Baraka Pedro Antonio
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

Customer churn remains a critical challenge for the telecommunications industry because customer attrition directly affects revenue, customer lifetime value, and long-term business sustainability. Accurate prediction of customers at risk of leaving is therefore essential for developing proactive retention strategies. This study proposes a hybrid wrapper-based feature selection framework that integrates Genetic Algorithm (GA) with Extreme Gradient Boosting (XGBoost) to improve customer churn prediction using the Telco Customer Churn Dataset. The proposed framework employs GA to identify the most informative feature subset through an evolutionary search process, followed by XGBoost classification on the optimized feature space. The novelty of this study lies in applying GA explicitly as a wrapper-based feature selection mechanism, rather than for hyperparameter optimization, enabling XGBoost to learn from a compact and highly discriminative set of customer attributes. Experimental results demonstrate that the proposed GA-XGBoost framework reduces feature dimensionality while achieving higher predictive performance than standalone XGBoost and several widely used machine learning classifiers under identical experimental conditions. These findings indicate that evolutionary feature optimization effectively enhances classification performance while reducing model complexity. The proposed framework provides a practical and computationally efficient solution for supporting data-driven customer retention strategies, enabling telecommunications providers to identify high-risk customers more accurately and optimize targeted retention campaigns.
Hybrid ant colony optimization and support vector machine for diabetes prediction Abayomi Adedamola Olayinka; Ayoola Oluwabukola Oyebanjo; Balarabe Usman Haruna; Chibuike Obinna Nwachukwu
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

Diabetes mellitus is one of the fastest-growing chronic diseases globally, with the International Diabetes Federation (IDF) reporting approximately 589 million adults affected in 2024 and projecting a rise to 853 million by 2050. Late diagnosis remains a critical barrier to effective management, motivating the development of accurate automated prediction systems. This study proposes a hybrid approach combining Ant Colony Optimization (ACO) as a wrapper-based feature selection method and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel as the classifier to predict diabetes risk. The Pima Indians Diabetes Dataset from the UCI Machine Learning Repository, comprising 768 samples with 8 clinical features and a binary class label (diabetic/non-diabetic), was used as the experimental benchmark. ACO was employed to identify the optimal feature subset by iteratively updating pheromone trails based on classification fitness, effectively reducing dimensionality while preserving discriminative information. The SVM model was subsequently trained on the five selected features to generate predictions. Experimental results demonstrate that the proposed ACO-SVM hybrid achieves an accuracy of 82.47%, precision of 79.31%, recall of 76.19%, and an F1-Score of 77.72%, outperforming standalone SVM (78.57%), PSO-SVM (80.52%), GA-SVM (79.87%), Random Forest (80.52%), Naïve Bayes (76.30%), KNN (74.03%), and Decision Tree (73.38%). These findings confirm that integrating colony-based metaheuristic optimization with SVM substantially improves diabetes prediction performance. Future work may explore adaptive ACO parameter tuning, larger and more diverse clinical datasets, and ensemble-learning integration to further enhance model robustness.
An explainable deep learning framework for autonomous drone-based military object detection using vision transformers Jontinus Manullang; R. Fanry Siahaan; Sutrisno Situmorang; Jonson Manurung
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 expansion of overhead imagery and unmanned aerial sensing has intensified the need for accurate, interpretable, and computationally adaptable object detection models in security-sensitive remote-sensing environments. The main research problem addressed in this study is the limited transparency of deep object detection models when identifying small, dense, and visually ambiguous objects in aerial or satellite imagery. This study aims to design an explainable deep learning framework that combines Vision Transformer representation learning, transfer learning, and Grad-CAM-based visual explanation for autonomous drone-based military object detection using the xView dataset. The proposed research process includes dataset preparation, image tiling, bounding-box conversion, normalization, transfer learning from an ImageNet-pretrained ViT backbone, transformer-based feature extraction, Grad-CAM visualization, and evaluation using accuracy, precision, recall, F1-score, IoU, AP, and mAP. The dataset used in this manuscript is the xView dataset available through a Kaggle mirror and originally introduced as a large-scale overhead imagery benchmark containing more than one million labeled object instances across 60 categories. Experimental results indicate that the proposed ViT + Grad-CAM framework achieves 0.823 accuracy, 0.814 macro precision, 0.806 macro recall, 0.810 macro F1-score, and 0.802 mAP@0.5 on a five-class defense-relevant subset, outperforming SSD, Faster R-CNN, and YOLOv8 baselines in the reported comparison. The study concludes that combining transformer-based global context modeling with explainability can improve both detection performance and model auditability in overhead imagery, although real deployment requires full-dataset validation, edge-device optimization, robustness testing, and strict human oversight for ethical use.

Page 3 of 3 | Total Record : 25

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