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Journal of Deep Learning, Computer Vision and Digital Image Processing
Published by Sakura Digital Nusantara
ISSN : 29868920     EISSN : 29868939     DOI : https://doi.org/10.61255/decoding
Core Subject : Science,
Computer Science & IT
The Journal of Deep Learning, Computer Vision and Digital Image Processing (DECODING), covers all topics of artificial intelligence and soft computing and their applications, including but not limited to: • Neural networks • Reasoning and evolution • Intelligent search • Intelligent planning • Intelligence applications • Computer vision and speech understanding • Multimedia and cognitive informatics • Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning • Technology and computing (like particle swarm optimization); intelligent system architectures • Knowledge representation • Bioinformatics • Natural language processing • Automated reasoning • Logic programming • Machine learning • Visual/linguistic perception • Evolutionary and swarm algorithms • Derivative-free optimisation algorithms • Fuzzy sets and logic • Rough sets • Simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc) • Multi-agent systems • Data and web mining • Emotional intelligence • Hybridisation of intelligent models/algorithms • Parallel and distributed realisation ofintelligent algorithms/systems • Application in pattern recognition, image understanding, control, robotics and bioinformatics • Application in system design, system identification, prediction, scheduling and game playing • Application in VLSI algorithms and mobile communication/computing systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 52 Documents
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Integration of named entity recognition and latent Dirichlet allocation for extracting cyberbullying issues on X Juanda Pratama; Defry Hamdhana; Zara Yunizar
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1528

Abstract

Purpose – The rapid growth of social media Platform X has increased the risk of cyberbullying, which is difficult to detect due to the unstructured nature of textual data. This study proposes an integration of Named Entity Recognition (NER) and Latent Dirichlet Allocation (LDA) to support the extraction of cyberbullying-related social issues.Methods – A total of 2,000 tweets were processed through preprocessing, spaCy-based entity extraction, TF-IDF weighting, and LDA topic modeling. The latent topics generated by LDA were manually mapped into four predefined categories (Bodyshaming, Racism, Gender, and Neutral) and evaluated against researcher-annotated ground truth labels.Findings – Experimental results achieved an overall accuracy of 80%, with F1-scores of 94% for Racism, 93% for Gender, 70% for Bodyshaming, and 63% for Neutral.Research implications – The proposed framework provides practical support for monitoring cyberbullying patterns and assisting policymakers in understanding online social issues.Originality – The originality of this research lies in the sequential integration of NER as an entity-filtering stage prior to LDA, enabling a more comprehensive analysis of cyberbullying discussions than the isolated application of either method.
Comparative Analysis of a Simple CNN and Fine-Tuned ResNet50 for Deepfake Image Detection: Performance and Computational Efficiency Evaluation Rifda Triani Mutmainah; Asti Herliana
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1629

Abstract

Purpose – Deepfake-related cybercrime is an increasingly troubling cybersecurity threat, since such content can now be produced from a single facial photo using freely available face-swapping techniques, while human ability to distinguish real from fake images remains limited (48.2–59%). This study compares a simple CNN and a fine-tuned ResNet50 within an identical, controlled framework.Methods – A controlled experiment compared a simple CNN (trained from scratch) and ResNet50 (two-phase fine-tuning) on 20,000 images from the Kaggle Deepfake and Real Images dataset (80:10:10 split, seed = 42). Candidate images were deduplicated before the data split; zero cross-split duplicates were confirmed. ResNet50 used ResNet-specific preprocessing; the CNN used inputs normalized to [0,1]. Evaluation used accuracy, precision, recall, F1-score, AUC-ROC, and specificity, with a paired McNemar's test as the primary significance measure.Findings – The CNN outperformed ResNet50 on six of seven metrics (accuracy 90.90% vs. 89.80%; AUC-ROC 97.08% vs. 96.52%), while ResNet50 achieved higher recall (93.10% vs. 91.80%). The accuracy difference was not statistically significant (p = 0.200). The CNN was 69.5 times smaller and trained faster (12.2 vs. 16.5 minutes). Neither model showed clear overfitting. Research implications – The findings rest on a single subset, a single split, and a single training run per model, limited to 20 epochs; the potential of ResNet50 with longer training has not been explored.Originality – This study combines accuracy, generalization-related diagnostics, and efficiency in a single controlled comparison, applies a paired test appropriate for a shared test set, and filters duplicates before the data split.

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