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All Journal Jurnal Ilmu Komputer dan Informasi Journal Automation Computer Information System (JACIS)
Okta Qomaruddin Aziz
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Library & Information Science

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Implementation of IndoBERT for Automatic Summarization of Friday Sermon Texts in Indonesian Language Sofwatul Ummah; Supriyono Supriyono; Okta Qomaruddin Aziz; Siti Annisa Rahmiasari; Taqiyyah Daaniys Shabrina Rusydiyyah
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.211

Abstract

Automatically transcribed Friday sermons are generally long and contain conversational noise, making their central messages difficult to locate quickly. This study implements and evaluates extractive summarization of Indonesian Friday-sermon transcripts using IndoBERT with sentence-level pseudo-labels. Of 210 YouTube transcripts, 32 documents that produced only one segment were excluded, leaving 178 documents and 16,131 sentences. The sermons were partitioned at document level into 124 training, 27 validation, and 27 test documents without overlapping doc_id values; TF-IDF was fitted only on 11,100 training sentences. Fine-tuning stopped at epoch 4 and selected the epoch-1 checkpoint with a training loss of 0.5128, validation loss of 0.4904, and validation accuracy of 0.7654. The checkpoint generated actual summaries for 27 test documents by selecting approximately 30% of their sentences. Against author-selected and author-reviewed extractive references, IndoBERT obtained ROUGE-1, ROUGE-2, and ROUGE-L F1 scores of 0.4141, 0.2907, and 0.3206. On the same documents, references, extraction ratio, and evaluation implementation, Lead-30% obtained 0.4559, 0.3024, and 0.2912, while LexRank obtained 0.6646, 0.5648, and 0.5890. LexRank achieved the strongest overall results, whereas IndoBERT exceeded Lead-30% on ROUGE-L. The study provides a controlled evaluation and empirical evidence about the limits of weakly supervised IndoBERT in the Friday-sermon domain
Endoscopic Image Classification Using ConvNeXt for GERD and Polyp Identification Muhammad Faqih; Okta Qomaruddin Aziz; Ajib Hanani
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i2.1702

Abstract

Early and accurate detection of gastrointestinal abnormalities, such as gastroesophageal reflux disease (GERD) and intestinal polyps, is essential for preventing severe clinical complications. However, manual interpretation of endoscopic images is often constrained by inter-observer variability and time limitations. This study proposes a ConvNeXt-Tiny-based deep learning framework for multi-class classification of gastrointestinal endoscopic images. Experiments were conducted using the GastroEndoNet v3 dataset, which contains 4,006 images categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. A total of twelve experimental scenarios were designed to systematically evaluate the effects of dataset-provided augmentation, ImageNet-based normalization, and batch size on model performance. The optimal configuration, combining augmentation, normalization, and a batch size of 64, achieved a test accuracy of 99.75% and a macro-averaged F1- score of 0.9977, indicating stable convergence and strong generalization on unseen data. The results demonstrate that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions. Comparative evaluation with a transformer-based baseline further indicates that modern convolutional architectures remain competitive for gastrointestinal image classification tasks. The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection. Extensions to video-based endoscopy would require incorporating temporal information across consecutive frames, which is beyond the scope of the current image-based study.
Co-Authors Ajib Hanani Muhammad Faqih Siti Annisa Rahmiasari Sofwatul Ummah Supriyono Taqiyyah Daaniys Shabrina Rusydiyyah