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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Sistem Informasi dan Teknologi Jaringan International Journal of Basic and Applied Science Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) DISTANCE: Journal of Data Science, Technology, and Computer Science Jurnal Mandiri IT Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Sistem Informasi Galuh Jurnal Ilmu Pendidikan dan Humaniora Jurnal Pengabdian Masyarakat Nauli Journal of Defense Technology and Engineering
Manurung, Jonson
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Journal : journal of intelligent decision support system idss

 1 
Comparison of k-means clustering with hierarchical agglomerative clustering for the analysis of food security of rice sector in Indonesia Sinaga, Ryan Fahlepy; M Azhar Prabukusumo; Manurung, Jonson
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.v8i1.290

Abstract

Indonesia's food security depends on the availability and distribution of rice as a staple food. To support data-driven policies, this study applies K-Means Clustering and Hierarchical Agglomerative Clustering (HAC) to cluster 38 provinces based on rice consumption and production patterns. Data is sourced from BPS with attributes: rice consumption per capita, rice production, rice price per kg, and population. These variables were chosen because they reflect the balance of demand, supply, affordability, and food needs. The optimal number of clusters was determined as three, based on Elbow Method and Silhouette Score for K-Means, and Dendrogram and Cophenetic Correlation Coefficient (CCC) for HAC. The clustering results identify regional characteristics related to food security and support the formulation of more targeted rice distribution policies. This study also compares the effectiveness of both methods in supporting equitable and sustainable food distribution strategies.
Mapping ownership of luxury goods and household assets in cities in Jawa Tengah using logistic regression Hanan, Rohman Ali; Firdaus, Eryan Ahmad; Manurung, Jonson
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.v8i1.292

Abstract

Ownership of luxury goods and household assets is a crucial issue in the Indonesian economy, particularly in Jawa Tengah, as it reflects complex socio-economic dynamics. This study aims to map the distribution of luxury goods and household assets across regencies and cities in Jawa Tengah and analyze the factors influencing their ownership using logistic regression. Socio-economic disparities in asset ownership are driven by factors such as education, income, and access to information, which contribute to broader social inequality and regional economic development.Using data from the Jawa Tengah Statistics Agency, this study examines variations in asset ownership, including motorcycles, refrigerators, and land, across different regions. Findings indicate that regions with higher motor vehicle ownership tend to exhibit stronger economic welfare compared to those with lower asset ownership. Beyond economic factors, psychological and social aspects, including social status and religious influences, also shape decisions regarding luxury goods acquisition.This research contributes to the literature by addressing the underexplored local context of asset ownership in Indonesia. The findings provide insights for policymakers in designing more inclusive and responsive socio-economic policies, aiming to reduce disparities and promote equitable regional development.
Implementation of TOPSIS method in decision support system for used motorcycle purchase recommendation Putra, Muhammad Ridho Alghifari; Manurung, Jonson; Hidayati, Ajeng
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.v8i2.289

Abstract

The selection of used motorcycles involves evaluating multiple criteria, such as price, production year, transmission type, vehicle type, mileage, fuel consumption, and engine capacity. This complex decision-making process often leads buyers to rely on subjective judgments or third-party recommendations, which may result in suboptimal choices. To address this issue, this research develops a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision Making (MCDM) method, which ranks alternatives based on their proximity to the ideal solution. The study introduces innovation by applying TOPSIS to the specific context of used motorcycle selection, providing a data-driven, objective approach in contrast to conventional methods. A quantitative approach was employed, with data collected from online marketplaces and authorized dealerships. The results indicate that the 2019 Honda Revo, priced at Rp. 8,600,000, is the most optimal choice, achieving the highest preference score of 0.862887804. The effectiveness of the TOPSIS method in structuring the selection process ensures a more systematic and accurate decision-making process. Furthermore, the study highlights the influence of key criteria, such as fuel efficiency and mileage, in determining the ranking of alternatives. Future research should focus on integrating additional factors, such as maintenance history and vehicle condition, and exploring the development of web-based or mobile platforms to improve real-world implementation and enhance user accessibility. This system contributes to smarter, more informed decision-making in the used vehicle market, offering a significant advancement over traditional selection methods.
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.
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 Adam Mardamsyah Adha, Rochedi Idul Agus Firmansyah Agustina Simangunsong Al Hashim, Safa Ayoub Amran Sitohang Andri Budiman, Mohammad anindito anindito Bagus Hendra Saputra Bagus Hendra Saputra Barus, Nadela Bosker Sinaga Bosker Sinaga Bosker Sinaga, Bosker Sinaga Br Sitepu, Siska Feronika Br Tarigan, Nera Mayana Dhaifullah, Rendi Hanif Erika Novianti Eryan Ahmad Firdaus Febrian Wahyu Christanto Ferdinand Tharorogo Wau Firdaus Laia Firdaus Situmorang Frainskoy Rio Naibaho Hanan, Rohman Ali Hardy Priyatno Ambarita Harpingka Sibarani Hasugian , Paska Marto Hengki Tamando Sihotang Hidayati, Ajeng Hoga Saragih Hondor Saragih I Made Aditya Pradhana Putra Jeremia Paskah Sinaga Johanes Perdamenta Sembiring Kadin Darlianto Tinambunan Kanur L. P. Situmorang Logaraj Logaraj Logaraj, Logaraj M Azhar Prabukusumo Mardamsyah, Adam Maria Siahaan Maya Theresia Br. Barus Maya Theresia Br. Barus Merlin Helentina Napitupulu Mina Kumari Muhammad Azhar Prabukusumo Muthmainnah, Ihmatull Nasyira, Muhammad Sulthan Nick Holson M. Silalahi Nuriansyah, Agam Pandiangan, Boyner Phatoni, Khaerul Imam Piliang, Rizqullah Aryaputra Poltak Sihombing Prabukusumo, M Azhar Prabukusumo, M. Azhar Prabukusumo Prabukusumo, Muhammad Azhar Pradhana Putra, I Made Aditya Putra, Muhammad Ridho Alghifari Ramen, Sethu Rinaldy Chaniago Sawaluddin Sawaluddin, Sawaluddin Sethu Ramen Sethu Ramen, Sethu Ramen Sidiq, Maulana Sigalingging, Miranda Bintang Maharani Sihombing, Agus Putra Emas Sihotang, Amran Silalahi, Monalisa Hotmauli Simangunsong, Humala Sinaga, Jeremia Sinaga, Jeremia Paska Sinaga, Ryan Fahlepy Sri Kumala Sari Tsany, Tazky Uzitha Ram Vernando, Deden