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All Journal Nuansa Informatika Journal of Defense Technology and Engineering
Rochedi Idul Adha
Universitas Pertahanan Republik Indonesia
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

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Web-Based Assignment Management System Application Design to Increase Personnel Productivity and Effectiveness in Product Documentation Division: Rancang Bangun Aplikasi Sistem Manajemen Penugasan Berbasis Web Guna Meningkatkan Produktifitas dan Efektifitas Personel Bagproddok Dispenad Seftian Candra Pratama; Eryan Ahmad Firdaus; Rochedi Idul Adha
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.368

Abstract

An effective task management is essential for enhancing productivity and operational efficiency within organizations. Currently, the Production Documentation Division of the Information Service of the Indonesian Army (Dispend) uses WhatsApp for task coordination, which results in fragmented communication, limited tracking, and reduced transparency. To overcome these issues, this study develops a web-based assignment management system  to improve task distribution, monitoring, and reporting. The system is built using the Rapid Application Development (RAD) model, which emphasizes iterative prototyping and users’ feedback. Key features include task creation, progress tracking, file submission, approval workflows, and reporting tools, supported by role-based access control for both administrators and users. The implementation of this system shows significant improvements in workflow efficiency, transparency, and accountability compared to the previous WhatsApp-based method. In conclusion, the system contributes to better documentation, timely task completion, and enhanced collaboration, thereby increasing overall productivity. Future enhancements may include mobile integration and real-time notifications to further improved accessibility and operational effectiveness.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Co-Authors Adam Mardamsyah Eryan Ahmad Firdaus Khaerul Imam Phatoni Seftian Candra Pratama