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All Journal Bulletin of Electrical Engineering and Informatics Journal of Data Insights
Hondor Saragih
Indonesia Defense University
Computer Science & IT Electrical & Electronics Engineering Mathematics

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A Classification of Debunking in Indonesian Fact-Checking Platforms Using NLP and Machine Learning : A Mixed-Methods Approach with Corpus Analysis and IndoBERT: Klasifikasi Pembantahan dalam Platform Pengecekan Fakta Indonesia Menggunakan NLP dan Machine Learning: Pendekatan Metode Campuran dengan Corpus Analysis dan IndoBERT Bayu Hartono; Riduan Riduan; Rudy Agus Gemilang Gultom; Hondor Saragih
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1182

Abstract

The rapid spread of disinformation through digital platforms constitutes a serious threat to social cohesion and public health. Debunking—the systematic refutation of false information using verified evidence—has emerged as a key countermeasure, yet manual identification and classification of debunking strategies is labor-intensive and difficult to scale. This study addresses this gap through a mixed-methods design integrating qualitative corpus analysis with automated machine learning (ML) classification. A corpus of 120 debunking articles published by three leading Indonesian fact-checking institutions (Kominfo AIS, Mafindo, and Cek Fakta Kompas, 2022–2024) was first manually annotated by two trained coders (Cohen's κ = 0.82) to identify four dominant debunking strategies: (1) contextual correction with emotional narrative framing; (2) source authority endorsement; (3) visual verification and reverse image search; and (4) myth-versus-fact inoculation format. This annotated corpus was subsequently used as a training dataset to develop and benchmark five NLP-based text classification models: TF-IDF + Support Vector Machine (SVM), TF-IDF + Random Forest, IndoBERT fine-tuned, IndoBERT with data augmentation (IndoBERT-Aug), and XGBoost with linguistic features. The IndoBERT-Aug model achieved the highest overall performance (macro-averaged F1 = 0.847, Precision = 0.851, Recall = 0.843), substantially outperforming the SVM baseline (F1 = 0.612). Logistic regression analysis further identified three significant moderators of debunking effectiveness: correction timeliness within 6 hours (OR=2.80, p<0.01), content readability (OR=0.68, p<0.01), and multi-platform distribution (OR=1.84, p<0.05), with the full model explaining 41% of variance (Nagelkerke R²=0.41). These contributions are formalized into the Indonesian Debunking Effectiveness Model (IDEM), a framework integrating automated strategy detection with evidence-based deployment guidelines for scalable counter-disinformation operations.
Optimization of cyber attack detection model using deep learning algorithm based on convolutional neural network Hondor Saragih; Jonson Manurung
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10579

Abstract

The increasing intensity and complexity of cyber threats demand more adaptive intrusion detection mechanisms. Conventional approaches are often limited in capturing complex and non-linear attack patterns in network traffic data. This study develops and evaluates a convolutional neural network (CNN)-based model for multi-class cyberattack detection. The proposed architecture integrates convolutional, pooling, and fully connected layers with rectified linear unit (ReLU) and SoftMax activation functions to improve classification performance. The network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset is used for training and evaluation. Experimental results show that the CNN model achieves 96.34% accuracy and an F1-score of 0.99, outperforming several traditional machine learning methods, including Naïve Bayes (NB), decision tree (DT), support vector machine (SVM), and random forest (RF). The superior performance is attributed to the model’s capability to automatically learn and extract meaningful spatial representations from network data without manual feature engineering. These findings demonstrate the effectiveness of deep learning techniques in improving cyberattack detection and contribute to the development of reliable AI-driven network security systems with strong potential for real-world cybersecurity applications and evolving threat mitigation strategies.
Co-Authors Bayu Hartono Jonson Manurung Riduan Riduan Rudy Agus Gemilang Gultom