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All Journal Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Studi Islam Lintas Negara (Journal of Cross-Border Islamic Studies) International Journal of Basic and Applied Science Jurnal Mandiri IT TRIMAS: Jurnal Inovasi dan Pengabdian Kepada Masyarakat Sahabat Sosial: Jurnal Pengabdian Masyarakat West Science Business and Management International Journal of Health Sciences Jurnal Sistem Informasi Galuh Jurnal Ilmiah Research Student Jurnal TNI Angkatan Udara Journal of Defense Technology and Engineering
Hondor Saragih
Universitas Pertahanan Republik Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation

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Automated news monitoring and sentiment analysis system using web scraping and large language models Naibaho, Zerusealtin David; Saragih, Hondor
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.
Blockchain-enhanced security framework for defense supply chain management: an AI-driven smart contract approach with distributed ledger technology Hondor Saragih; Jonson Manurung; Hengki Tamando Sihotang; I Made Aditya Pradhana Putra
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

Defense supply chains face critical security challenges including counterfeit components, unauthorized access, data tampering, and supply chain attacks that compromise operational integrity and national security. Existing blockchain implementations suffer from limited scalability, inadequate threat detection mechanisms, and insufficient integration with modern AI technologies for real-time security monitoring. This research develops an AI-Enhanced Blockchain Security Framework combining smart contracts with distributed ledger technology specifically designed for defense supply chain management. The framework employs multi-signature authentication, cryptographic verification, and machine learning-based anomaly detection across a three-layer architecture (blockchain layer, security layer, analytics layer). Validation using the DataCo supply chain dataset (180K operations) and Backstabber's knife collection attack patterns (174 documented attacks) demonstrates 94.7% attack detection accuracy, 87.3% reduction in unauthorized access attempts, and 99.2% data integrity verification rate. The system achieved 850 transactions per second (TPS) throughput with 1.8-second average latency and 40% cost reduction compared to traditional centralized systems. Smart contract execution showed 99.96% reliability across 10,000 test scenarios with automated enforcement of security policies. Statistical validation confirmed significant superiority over conventional approaches (p<0.001). Future work includes quantum-resistant cryptography, federated learning for privacy-preserving analytics, cross-chain interoperability, and integration with IoT sensors for real-time supply chain monitoring.
Disinformation propagation modeling in digital information warfare using hybrid GNN and LSTM Manurung, Jonson; Saragih, Hondor; Mardamsyah, Adam; Sinaga, Jeremia Paska
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 Saragih, Hondor; Saragih, Hoga; Manurung, Jonson; Adha, Rochedi Idul; Naibaho, Frainskoy Rio
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.
Co-Authors Adha, Rochedi Idul Ajeng Hidayati Anindito Anindito, Anindito Anisa Purnamasari Anjani, Prasashti Alya Arif Rachman Asmi, Syamsinar Aulia Khamas Heikmakhtiar Chan, Ricky Cut Maisyarah Karyati Dewi Parwati Dhaifullah, Rendi Hanif Erika Erika Eryan Ahmad Firdaus Febrian Wahyu Christanto Fitriana Fitriana Frainskoy Rio Naibaho Gandhara, Seto Harfika, Meiana Heikhmakhtiar, Aulia Khamas Hendra Manurung, Hendra Hengki Tamando Sihotang Herris, Fhatur Robby Tanzil Hidayati , Ajeng Hidayati, Ajeng Hoga Saragih Hutabarat, Erny I Made Aditya Pradhana Putra Iman Saufik Suasana Lediwara, Nadiza Mainassy, Meilisa Carlen Makualaina, Fenska Narly Manurung, Jonson Mardamsyah, Adam Marunung, Hendra Meillisa Carlen Mainassy Muhadi Sugiono Muhamad Risal Tawil Muhammad Naufal Nafian Abror Mulat, Trimaya Cahya Naibaho, Zerusealtin David Naufal Rizki, Muhammad Nurul Aisyiyah Puspitarini P, Nur Triningtias Pannyiwi, Rahmat Prabukusumo, Muhammad Azhar Pradhana Putra, I Made Aditya Putri, Nanda Selviana Putri, Noviyanti Rahardjo Rahmat, Rezqiqah Aulia Rezasyah, Teuku Ricky Chan Sadewa, Rio Bintang Saputra, Bagus Hendra Saputra, M. Khalid Fredy Satria, Tegar Pandu Sembada Denrineksa Bimorogo Shanti Maulani Simarmata, Dicky Daniel Sinaga, Jeremia Paska Sipahutar, Pongki Sitanggang, Johan Adrian Sofiana Sofiana, Sofiana Tegar Pandu Satria Treasa, Adisty Dwi Vernando, Deden Zefanya Seto Gandhara