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All Journal El-Hayah : Jurnal Biologi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Psikoislamika : Jurnal Psikologi dan Psikologi Islam IT JOURNAL RESEARCH AND DEVELOPMENT J-Dinamika: Jurnal Pengabdian Kepada Masyarakat EDUTEC : Journal of Education And Technology ILKOMNIKA: Journal of Computer Science and Applied Informatics Computer Science and Information Technologies Jurnal Pendidikan Islam Al-Affan Journal of Information Technology and Cyber Security
Mochamad Imamudin
UIN Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Mathematics Medicine & Pharmacology Neuroscience Public Health Other

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Journal : ILKOMNIKA: Journal of Computer Science and Applied Informatics

 1 
Fine-Tuned BART Transfer Learning for Abstractive Summarization of Indonesian YouTube Transcripts with ROUGE Evaluation Setiawan, Diyan Nova; Faisal, Muhammad; Imamudin, Mochamad
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.862

Abstract

Indonesian YouTube transcripts present substantial challenges for abstractive summarization because they contain informal expressions, filler words, fragmented utterances, and automatically generated caption errors. Existing Indonesian summarization studies have mostly focused on formal written texts such as news articles, while duration-aware abstractive summarization of noisy Indonesian educational video transcripts remains underexplored. This study fine-tunes a BART-based sequence-to-sequence model using a curated corpus of Indonesian YouTube transcripts from the “Kok Bisa?” educational channel. From 1,000 collected videos, 957 transcripts were successfully retrieved, and 730 transcripts passed the final filtering criteria for experimental analysis. The dataset was divided into short, medium, and long transcript categories to evaluate the effect of input duration on summarization quality. The proposed pipeline includes transcript retrieval, metadata extraction, text normalization, filler-word removal, repetition filtering, BART fine-tuning, summary generation, and ROUGE-based evaluation. The model achieved the best performance on short transcripts, with ROUGE-1 F1 = 0.621, ROUGE-2 F1 = 0.438, and ROUGE-L F1 = 0.587. Performance decreased on long transcripts, with ROUGE-1 F1 = 0.552, ROUGE-2 F1 = 0.384, and ROUGE-L F1 = 0.509, indicating that longer narratives reduce lexical and structural alignment. These findings show that fine-tuned BART is effective for short Indonesian educational transcripts but requires segmentation, semantic evaluation, and stronger baseline comparison for long-form video summarization.
GA-Optimized Stacking Ensemble for Unified Phishing Website and Email Detection Using Multi-Domain Feature Representation Hermawan, Hendra; Faisal, Muhammad; Imamudin, Mochamad
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.864

Abstract

Phishing remains a major cybersecurity threat because attacks increasingly combine fraudulent websites with deceptive email content. Existing detection models often focus on a single domain, such as URLs or emails, which limits their ability to capture heterogeneous phishing patterns. This study proposes a GA-optimized stacking ensemble framework for unified phishing website and email detection using multi-domain features. The framework combines URL structural attributes, email metadata, and semantic content features, while a Genetic Algorithm is used to reduce feature redundancy and select the most informative attributes. The proposed model is evaluated against baseline Random Forest, Gradient Boosting, and conventional Stacking classifiers using Accuracy, F1-score, AUC-ROC, cross-validation stability, robustness under noise, and inference latency. Experimental results show that the proposed GA-Stacking model achieves 98.1% accuracy, 97.6% F1-score, and 0.947 AUC-ROC, outperforming Random Forest, Gradient Boosting, and standard Stacking models. The model also reduces the feature set from 72 to 31 features and maintains strong robustness under simulated noise, with F1-score remaining at 92.0% under 30% perturbation. These findings indicate that evolutionary feature optimization improves the stability, efficiency, and robustness of stacking ensemble learning for multi-domain phishing detection.
Co-Authors A'la Syauqi Abd. Rouf Agung Teguh Wibowo Almais Akbar Roihan AUNUR ROFIQ Aziz, Kevin Elmy Cahyo Crysdian Dian Maharani, Dian Dyah Febriantina Istiqomah Fresy Nugroho Hammad, Jehad Abdelhamid Hermawan, Hendra KHOIRUL ANAM Lestari, Tri Mukti Muhammad Faisal Nurrahman, Alfina Pebrianti, Dwi Putra, Caesar Nafiansyah Putri Purnamasari Setiawan, Diyan Nova Shinta Rizki Firdina Sugiono Syafiqah, Annisa Syahiduz Zaman Syauqi, A’la