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All Journal International Journal of Enterprise Modelling Journal of Defense Technology and Engineering
Bagus Hendra Saputra
Universitas Pertahanan Republik Indonesia, Bogor, 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 Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Transportation

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Multimodal deep learning framework for detection and attribution of adversarial information operations on social media platforms Nick Holson M. Silalahi; Jonson Manurung; Bagus Hendra Saputra
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

Adversarial information operations on social media platforms pose critical threats to national security, with state-sponsored actors exploiting multimodal content manipulation to conduct sophisticated disinformation campaigns. Existing detection approaches focus on single-modality analysis, lacking comprehensive frameworks for simultaneous detection, attribution, and coordination identification. This research develops an integrated multimodal deep learning framework combining RoBERTa-large transformer, Vision Transformer, Graph Convolutional Networks, and bidirectional LSTM, unified through cross-modal attention fusion with multi-task learning optimization. Experimental validation utilizes eight datasets including Russian IRA tweets (3.8M posts), Fakeddit (1M submissions), TweepFake (25K accounts), FakeNewsNet (23K articles), MM-COVID (6.7K posts), CREDBANK (60M tweets), and MEMES (12K items). Results demonstrate 93.24% detection accuracy, 79.34% attribution accuracy across 15 threat actor groups, 91.67% coordination F1-score, 88.62% narrative classification accuracy, and 448ms inference latency suitable for real-time deployment. Ablation studies reveal graph neural networks provide largest performance contribution (5.82% improvement), highlighting social network analysis importance for detecting coordinated behavior. Future directions include large-scale pre-training, adversarial training, continual learning, human-AI collaboration, multilingual expansion, federated learning, and causal inference methods.
Content-Based Filtering Using TF-IDF for a Course Recommendation System for Indonesian MSME Entrepreneurs Rr Octanty Mulianingtyas; Bagus Hendra Saputra; Rohani Situmorang; Galih Prakoso Rizky A
International Journal of Enterprise Modelling Vol. 20 No. 2 (2026): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i2.172

Abstract

Micro, Small, and Medium Enterprises (MSMEs), particularly those led by female entrepreneurs, play a vital role in Indonesia’s economic development; however, digital learning platforms often lack adaptive mechanisms that align course offerings with individual learning needs. Existing platforms generally rely on manual selection or generic categorization, creating a gap in personalized recommendation support within digital entrepreneurship education. The primary objective of this study is to develop and assess a content-based course recommendation system for Femalepreneur.id that addresses this limitation. A quantitative experimental research design was adopted using user profile data collected through questionnaires and course descriptions obtained from the platform repository. The methodology integrates systematic text preprocessing, feature representation using Term Frequency–Inverse Document Frequency (TF-IDF), and similarity computation through cosine similarity to generate personalized recommendations. The experimental results indicate Mean Precision@3 at 51.5%, Mean Average Precision@3 (MAP@3) at 42.9%, and Hit Ratio@3 at 90.9%. The precision matrix demonstrates the system can recommend relevant result until three courses as the maximum value based on the ground truth.  While, Hit Ratio matrix reveals that at least the system can recommend at least one relevant topic.  These findings confirm the effectiveness of TF-IDF in modelling textual learning features and highlight the contribution of the proposed system in strengthening personalized digital entrepreneurship learning for female entrepreneurs.
Human object detection and classification system based on thermal cameras using the YOLOv11 object detection model Muhammad Irsyaad Nurrahman; Bagus Hendra Saputra; H. A. Danang Rimbawa
International Journal of Enterprise Modelling Vol. 20 No. 2 (2026): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i2.188

Abstract

Strategic institutions such as military campuses, defense research centers, and government facilities face increasingly complex security challenges, particularly in environments with low visibility and limited manual patrol capabilities. Conventional surveillance systems often perform poorly in dark environments because they depend heavily on visible light. Therefore, this research proposes a human object detection and classification system based on thermal cameras integrated with the YOLOv11 object detection model. Thermal cameras are capable of capturing heat radiation emitted by objects, enabling effective visualization under low-light and completely dark conditions. The proposed system combines thermal imaging technology with the real-time detection capability of YOLOv11 to automatically identify and classify human objects. This research employs the Research and Development (R&D) method, including dataset collection, image annotation, data augmentation, data preprocessing, model training, and system evaluation. The dataset consists of thermal images enhanced using augmentation techniques such as cropping, rotation, brightness adjustment, and blur effects to improve model robustness. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix analysis. Experimental results demonstrate that the proposed system achieved an average accuracy of 86.36%, with accuracy values of 85.98% under completely dark conditions and 86.75% under dim-light conditions, indicating that the model is capable of reliably detecting and classifying human objects in low-visibility environments. These findings show that the integration of thermal cameras and YOLOv11 can contribute to the development of intelligent security systems that improve surveillance efficiency while reducing dependence on manual monitoring.
Co-Authors Galih Prakoso Rizky A H. A. Danang Rimbawa Manurung, Jonson Muhammad Irsyaad Nurrahman Nick Holson M. Silalahi Rohani Situmorang RR Octanty Mulianingtyas