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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics
Najib, Jihan
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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 that contains numerous events, figures, locations, and chronological relationships. These characteristics mean that summarisation methods relying solely on word frequency or lexical similarity may overlook historically significant sentences. This study proposes Hybrid TextRank for the extractive summarisation of Indonesian-language Sirah Nabawiyah texts. The proposed method combines TF-IDF and BM25 similarities as graph edge weights and incorporates sentence position and historical entity density into the PageRank personalisation vector. The dataset comprises 312 subchapters from two parts of the Sirah Nabawiyah book. Quantitative evaluation was conducted on 157 subchapters from Part 1, which include human reference summaries, while 155 subchapters from Part 2 were used as a supporting corpus. Each method selected three sentences and was evaluated using ROUGE-1, ROUGE-2, ROUGE-L, and intra-summary similarity. Hybrid TextRank achieved F1 scores of 0.3353 for ROUGE-1, 0.1178 for ROUGE-2, and 0.2264 for ROUGE-L. Compared with standard TF-IDF-based TextRank, these results represent improvements of 6.28%, 12.73%, and 8.59%, respectively. Hybrid TextRank achieved the highest ROUGE-2 score, although Max-TFIDF slightly outperformed it on ROUGE-1 and ROUGE-L. These findings indicate that personalisation based on narrative structure and historical entities helps preserve important word pairs, while sentence selection still requires further optimisation to improve unigram coverage and overall sentence ordering.
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 Davissyah, Asfa Lestari, Tri Mukti Octadaniswara, Daffa Andika Okta Qomaruddin Aziz Supriyono Supriyono