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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics
Davissyah, Asfa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Extractive Summarization of Indonesian Sirah Nabawiyah Texts Using a Hybrid TextRank Algorithm Davissyah, Asfa; Supriyono, Supriyono; Lestari, Tri Mukti; Octadaniswara, Daffa Andika; Najib, Jihan
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.931

Abstract

Sirah Nabawiyah is a religious narrative text containing numerous events, figures, locations, and chronological relationships. These characteristics may limit summarization methods that rely solely on word frequency or lexical similarity. This study proposes Hybrid TextRank for extractive summarization of Indonesian Sirah Nabawiyah texts. The method combines TF-IDF and BM25 similarities as graph edge weights and incorporates sentence position and historical-entity weighting into a personalized PageRank mechanism. The dataset comprises 312 subchapters from two parts of Ar-Raheeq Al-Makhtum. Quantitative evaluation was conducted on 157 subchapters from Part 1 with human reference summaries, while 155 subchapters from Part 2 were used as a supporting corpus. All methods generated three-sentence summaries and were evaluated using ROUGE-1, ROUGE-2, ROUGE-L, and intra-summary similarity. Using an empirically selected damping factor of 0.70, Hybrid TextRank achieved F1 scores of 0.3401, 0.1216, and 0.2340 for ROUGE-1, ROUGE-2, and ROUGE-L, respectively. Hybrid TextRank achieved the highest ROUGE-2 and ROUGE-L scores, whereas Max-TFIDF obtained the highest ROUGE-1 score. Statistical testing confirmed these metric-specific differences, while ablation analysis showed that the complete Hybrid TextRank configuration outperformed its partial component combinations. These findings indicate that combining hybrid lexical similarity with domain-aware personalization improves phrase-level and sequence-level similarity in extractive summarization of Sirah Nabawiyah texts.
Multi-Class Humor Level Classification of Indonesian Stand-Up Comedy Transcripts Using Fine-Tuned IndoBERT Najib, Jihan; Supriyono, Supriyono; Aziz, Okta Qomaruddin; Andika, Daffa; Davissyah, Asfa
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.935

Abstract

Stand-up comedy has become a popular form of entertainment in Indonesia, but humor assessment remains subjective and is generally performed manually. This study develops an automatic classification model for Indonesian stand-up comedy humor levels using transcripts and the IndoBERT language model. The dataset was collected from stand-up comedy videos on the Kompas TV YouTube channel and used audience laughter counts as a pragmatic indicator of humor response. After removing records without transcripts and duplicate transcripts, 2,774 independent records were obtained and categorized into four humor levels: Not Funny, Slightly Funny, Funny, and Very Funny. The dataset was divided into training, validation, and test sets using an 80:10:10 stratified split. IndoBERT was fine-tuned and compared with a majority-class classifier and TF-IDF-based conventional baselines. On the test set, IndoBERT achieved 67.99% accuracy, 67.83% weighted precision, 67.99% weighted recall, and 67.19% weighted F1-score, outperforming the strongest conventional baseline by 11.87 percentage points. Cohen’s Kappa values were 0.5647 unweighted and 0.8309 with quadratic weighting. Moreover, 91.0% of misclassifications occurred in adjacent categories, indicating that the model captures the ordinal structure of humor levels. These results demonstrate that IndoBERT provides a viable baseline for Indonesian humor-level classification, although distinguishing between adjacent humor categories remains challenging.
Co-Authors Andika, Daffa Lestari, Tri Mukti Najib, Jihan Octadaniswara, Daffa Andika Okta Qomaruddin Aziz Supriyono Supriyono