Article Per Year (5 Year)
p-Index From 2021 - 2026
4.602
P-Index
This Author published in this journals
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

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : journal of intelligent decision support system idss

 1 
The development of a data lakehouse system for the integration and management of cyber threat intelligence data in XYZ unit Chan, Ricky; Dhaifullah, Rendi Hanif; Saragih, Hondor; Lediwara, Nadiza; Adha, Rochedi Idul
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.293

Abstract

Cybersecurity systems are evolving to deal with increasingly complex digital threats. One of the main challenges in this field is integrating and managing Cyber Threat Intelligence (CTI) efficiently. This research aims to design and implement Data Lakehouse as a solution to manage CTI data in XYZ Unit. The system was built using Apache Spark, MinIO, Dremio, Nessie, and Apache Iceberg with a containerization approach using Docker to ensure flexibility and ease of implementation. The implementation results show that the system successfully integrates various CTI data sources and improves efficiency in data storage, processing, and analysis. MinIO is used as the primary storage, Apache Spark processes data at scale, Dremio enables real-time data analysis, and Nessie manages data version control to maintain its integrity. Blackbox testing proves that the system can work optimally, with results showing improved data integration and efficiency in managing cyber threat information. Thus, the developed Data Lakehouse can be an effective solution in supporting threat detection and strategic decision-making in XYZ Unit.
Heart disease prediction using machine learning models Vernando, Deden; Manurung, Jonson; Saragih, Hondor
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: 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.v8i2.291

Abstract

Heart disease remains one of the leading causes of death globally, with mortality rates continuing to rise each year. Early detection is critical to reducing the burden of this disease; however, conventional diagnostic methods are often costly, time-consuming, and reliant on specialist expertise. This study aims to evaluate the effectiveness of four machine learning (ML) algorithms—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—in predicting heart disease using clinical datasets. The methodology involves data preprocessing, feature selection using the Random Forest algorithm, and performance evaluation through metrics such as accuracy, precision, recall, F1-score, and support. Experimental results indicate that KNN achieved the highest accuracy after feature selection, while SVM demonstrated the highest recall despite lower precision. RF offered the most balanced performance, making it a reliable model for real-world medical applications. These findings highlight the importance of selecting appropriate algorithms and features to improve the performance of predictive models. The study suggests that future research should incorporate larger datasets, apply systematic hyperparameter tuning, and explore deep learning techniques to further enhance prediction accuracy.
Web-based development of room management information system at Universitas Pertahanan using Rapid Application Development Anjani, Prasashti Alya; Saragih, Hondor; Hidayati, Ajeng; Anindito
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 3 (2024): 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.v7i3.254

Abstract

Unhan RI is an educational institution responsible for facilitating the continuity of student’s academic activites, including the scheduling process that managed by the department’s staff. The scheduling process requires components such as courses, lectures, time slots, and the classrooms. The number of available classrooms at Unhan RI is less than it need. Therefore, a proper scheduling system is necessary to manage scheduling and avoid conflicts between schedule. The development of information management system for administration’s process that are still done manually are needed in this digital era. Because the large and continuously growing amount of data is difficult to process manually. The development is using Rapid Application Development method. This method is chosen because of the requirement time for the developing is short.  By using the room management information system, the process of scheduling courses and managing rooms can be done easily. This system provides information of room availability and ongoing activites, helping to prevent scheduling conflicts.
Comparison of Naïve Bayes Classifier and Support Vector Machine for sentiment analysis on civil military relations conflict among Rohingya refugees as recommendation for defense policy making Putri, Nanda Selviana; Saragih, Hondor; Heikhmakhtiar, Aulia Khamas
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 3 (2024): 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.v7i3.255

Abstract

This research focuses on the evaluating the performance of various sentiment analysis techniques using the Naive Bayes Classifier and Support Vector Machine in identifying civil-military conflicts among Rohingya refugees. The goal is to assist leaders in formulating defense policies. This research uses text data from news sources on Twitter, with a total of 5018 data that have been processed to become clean data, then divided into 1004 test data and 4018 training data to be classified using the Support Vector Machine and Naive Bayes methods. This research analyzes the sentiment and polarity of public opinion related to the issues that occur in this situation. The results of the sentiment analysis from the two methods are then classified using the Support Vector Machine and Naive Bayes methods, and then compared to determine which method is more effective in capturing the complex dynamics of sentiment. The findings of this research indicate that the Support Vector Machine method has a higher accuracy in identifying sentiments related to the civil-military conflict among Rohingya refugees, with an accuracy of 87.95%, compared to the Naive Bayes Classifier with an accuracy of 85.16%. The analysis results in the form of frequently occurring words in the true positive word cloud, namely apology, human, angry, and solidarity, are handed over to experts to be formulated into recommendation sentences and can be used to assist in the formulation of policies for defense decision-makers in more effectively addressing the Rohingya refugee issue.
Generative AI and multi-source intelligence for automated security triage Herris, Fhatur Robby Tanzil; Saragih, Hondor; Anindito, Anindito
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 4 (2025): December: 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.v8i4.326

Abstract

Security Operation Center (SOC) analysts encounter significant delays due to "Swivel Chair Analysis," a manual and fragmented process for triaging Indicators of Compromise (IoC). This study addresses this inefficiency by developing "CyberGuardianBot," an automated ChatOps assistant built using the Rapid Application Development (RAD) methodology and the Telegram Bot API. Applying Security Orchestration, Automation, and Response (SOAR) principles, the system asynchronously orchestrates multi-source intelligence from VirusTotal, AbuseIPDB, URLScan.io, AlienVault OTX, and MobSF. A key novelty is the integration of Google Gemini to perform cognitive synthesis, translating raw API data into actionable insights. Blackbox testing validated the system across 15 test cases, confirming the successful automation of URL, IP, and file triage. The bot generates natural language executive summaries and structured reports (.txt and .pdf), significantly enhancing the speed and accuracy of the triage process while reducing the cognitive load on analysts.
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 C. 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