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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT Jurnal Pengabdian Masyarakat Nauli
Hondor Saragih
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Disinformation propagation modeling in digital information warfare using hybrid GNN and LSTM Jonson Manurung; Hondor Saragih; Adam Mardamsyah; Jeremia Paska Sinaga
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.345

Abstract

The rapid growth of digital information warfare has enabled the widespread dissemination of disinformation, posing serious challenges for detection systems. However, most existing approaches treat disinformation detection as a static classification problem and fail to consider the network structure and temporal dynamics of information spread. This study proposes a hybrid deep learning model that combines Graph Attention Networks (GAT) and Bidirectional Long Short-Term Memory (BiLSTM) with a cross-attention mechanism to capture both structural and temporal patterns of disinformation propagation.  The proposed model was evaluated using three datasets: the PHEME rumor dataset, a large-scale Twitter and X crisis dataset, and a synthetically generated defense simulation dataset. Experimental results show that the model achieves strong performance, with 92.47% accuracy in classification, 89.63% precision in cascade prediction, 87.91% F1-score in source identification, and a mean absolute error of 0.183 in predicting spread dynamics, outperforming several baseline methods. These findings demonstrate that integrating network-based and temporal modeling can significantly improve disinformation detection performance. Future research will focus on incorporating multimodal data, real-time processing, and cross-platform learning to enhance the robustness of the proposed approach.
Distributed cyber defense framework based on federated learning for attack detection in defense infrastructure Hondor Saragih; Hoga Saragih; Jonson Manurung; Rochedi Idul Adha; Frainskoy Rio Naibaho
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.346

Abstract

Cyber threats targeting defense infrastructure have escalated in complexity, rendering centralized intrusion detection systems insufficient due to their inability to guarantee data privacy across distributed military nodes. This study proposes a distributed cyber defense framework that employs federated learning to enable collaborative model training without transmitting raw network traffic beyond individual nodes. The framework integrates an adaptive aggregation strategy combining FedAvg and FedProx, a hybrid deep learning architecture consisting of convolutional neural networks and long short term memory networks, an autoencoder module for unsupervised anomaly detection, a Byzantine robust aggregation mechanism, and post hoc explainability through SHAP and LIME. Experiments were conducted on CIC IDS 2017, CIC IDS 2018, UNSW NB15, and a synthetically generated military network traffic dataset. The proposed framework attained a peak accuracy of 98.74% and an F1 score of 98.12% on CIC IDS 2017, consistently outperforming five baseline methods by up to 5.29 percentage points in F1 score. Future work will investigate differential privacy integration and model compression for deployment on resource constrained tactical edge devices.
Pemberdayaan Masyarakat melalui Edukasi Etika Bermedia Sosial di Desa Tajur Halang Pada SMPN 1 Cijeruk Kabupaten Bogor Eryan Ahmad Firdaus; Hondor Saragih; Jonson Manurung; Muhammad Azhar Prabukusumo; Ajeng Hidayati; Nisrina Labiba Sarwoko
Jurnal Pengabdian Masyarakat Nauli Vol. 4 No. 2 (2026): Februari, Jurnal Pengabdian Masyarakat Nauli
Publisher : Marcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/nauli.v4i2.298

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan oleh Dosen dan mahasiswa Program Studi Informatika Fakultas Teknik dan Teknologi Pertahanan Universitas Pertahanan Republik Indonesia di Desa Tajur Halang, khususnya di SMPN 1 Cijeruk. Kegiatan difokuskan pada sosialisasi etika bermedia sosial dengan penekanan pada bahaya oversharing dan dampaknya terhadap keamanan siber. Tujuan kegiatan ini adalah meningkatkan pemahaman serta kesadaran siswa dalam menggunakan media sosial secara bijak, bertanggung jawab, dan aman. Metode pelaksanaan meliputi penyampaian materi, diskusi interaktif, serta sesi tanya jawab untuk mendorong partisipasi aktif siswa. Hasil kegiatan menunjukkan bahwa siswa memperoleh peningkatan pemahaman mengenai etika digital, pentingnya menjaga privasi, serta kesadaran terhadap jejak digital yang bersifat permanen. Selain itu, siswa menjadi lebih waspada terhadap risiko keamanan siber seperti phishing dan pencurian identitas. Kegiatan pengabdian kepada masyarakat ini diharapkan dapat memberikan kontribusi positif dalam membentuk perilaku bermedia sosial yang sehat dan aman di kalangan pelajar.
Design and development of the spacelog web application for inventory management and asset tracking using QR codes at the Cyber Defense Center of the Ministry of Defense Johan Adrian Sitanggang; Bagus Hendra Saputra; Ajeng Hidayati; Hondor Saragih
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.481

Abstract

SpaceLog is a web-based inventory information system developed for the Cyber Defense Center of the Indonesian Ministry of Defense to address the limitations of spreadsheet-based management, which is static, non-real-time, and lacks accountability. This study proposes a novel approach by implementing a unit-centric architecture combined with Role-Based Access Control (RBAC) specifically tailored for the high-security requirements of the defense sector. The system development utilizes the Rapid Application Development (RAD) method, built upon Laravel, MySQL, and Bootstrap frameworks. Key features include unique QR Code tracking for individual assets, hierarchical location mapping, and a comprehensive audit trail. Testing results using the Black-Box method demonstrate that all functional scenarios, including item tracking and tiered access rights (Superadmin, Section Head, Staff), operate with 100% validity. Furthermore, the implementation significantly improves operational success by transforming asset management from a manual, error-prone process into a real-time, fully auditable digital ecosystem, thereby meeting the strict accountability standards of the Ministry of Defense.
Mixed integer linear programming for cadet dormitory placement at Indonesia Defense University I Made Aditya Pradhana Putra; Jonson Manurung; Hondor Saragih
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.487

Abstract

Cadet dormitory placement at Indonesian Defense University was currently performed manually by administrative staff, resulting in potential inefficiencies in room assignments regarding walking distance, study program cohesion, and cadet preferences. This research developed a Mixed Integer Linear Programming (MILP) optimization model to automate and improve the dormitory assignment process for military education institutions. The general framework addresses 1,550 cadets distributed across four cohorts and 13 study programs in   dormitory buildings with standardized configurations (3 floors, 25 rooms per floor, 2 cadets per room). The MILP model incorporated three objectives: minimizing total walking distance to academic facilities, maximizing study program cohesion by concentrating programs within specific floors, and maximizing cadet floor preference satisfaction. The model was formulated with configurable weight parameters (w₁, w₂, w₃) enabling administrators to balance competing objectives according to institutional priorities. A validation case study with 38 male cadets from two study programs demonstrated computational feasibility, with the CBC solver achieving optimal solutions in 0.34 seconds (strict constraint approach) and 0.11 seconds (maximum occupancy approach) on standard desktop hardware, both with 0.00% MIP gap confirming proven optimality. The validation study compared two policy approaches: strict constraint enforcement achieving 95% room occupancy with 20 rooms, and maximum space utilization achieving 100% occupancy with 19 rooms. This research contributed the first application of MILP optimization to military education dormitory management in Indonesia, providing a scalable framework with empirical validation for computational tractability and a replicable methodology for resource allocation optimization in defense institutions.
Automated news monitoring and sentiment analysis system using web scraping and large language models Zerusealtin David Naibaho; Hondor Saragih
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.492

Abstract

Organizations increasingly require efficient systems to monitor and analyze vast online news data for timely and informed decision making. Manual monitoring is inadequate due to information overload and the time sensitive nature of digital content. This study presents the design, development, and evaluation of an automated web based news monitoring and sentiment analysis system integrating web scraping and artificial intelligence. The system was implemented using the Django web framework with a PostgreSQL database, Playwright browser automation for dynamic content extraction, and Google’s Gemini API for contextual sentiment classification. Three main functions were developed: automated data collection based on keywords and date ranges, AI driven sentiment analysis producing positive, negative, or neutral labels with contextual understanding, and automated reporting with interactive visualizations exportable to XLSX and CSV formats. Functional black box testing confirmed 100% success across 28 test cases, verifying reliability in authentication, data acquisition, sentiment analysis, and visualization. Performance evaluation showed that the system could collect 50–200 articles within 2–4 minutes and process sentiment analysis at 1–2 seconds per article. The proposed system effectively transforms manual workflows into fully automated operations, enabling systematic media monitoring, sentiment tracking, and data driven decision support.
Co-Authors Adam Mardamsyah Ajeng Hidayati Bagus Hendra Saputra Eryan Ahmad Firdaus Frainskoy Rio Naibaho Hoga Saragih I Made Aditya Pradhana Putra Jeremia Paska Sinaga Johan Adrian Sitanggang Manurung, Jonson Muhammad Azhar Prabukusumo Nisrina Labiba Sarwoko Rochedi Idul Adha Zerusealtin David Naibaho