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All Journal IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Transformasi: Jurnal Pengabdian Masyarakat JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal ULTIMATICS bit-Tech Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Abdi Teknoyasa Artificial Intelligence Systems and Its Applications (AISA)
Endang Wahyu Pamungkas
Universitas Muhammadiyah Surakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science

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Journal : JOIN (Jurnal Online Informatika)

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Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts Wiratmoko, Galih; Thamrin, Husni; Pamungkas, Endang Wahyu
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1506

Abstract

Automatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. Indonesian presents unique linguistic challenges, including its agglutinative structure, borrowed vocabulary, and limited availability of high-quality training data. This study conducts a comparative evaluation of extractive, abstractive, and hybrid models for Indonesian text summarization, utilizing the IndoSum dataset which contains 20,000 text-summary pairs. We tested several models including LSA (Latent Semantic Analysis), LexRank, T5, and BART, to assess their effectiveness in generating summaries. The results show that the LexRank+BERT hybrid model outperforms traditional extractive methods, achieving better ROUGE precision, recall, and F-measure scores. Among the abstractive methods, the T5-Large model demonstrated the best performance, producing more coherent and semantically rich summaries compared to other models. These findings suggest that hybrid and abstractive approaches are better suited for Indonesian text summarization, especially when leveraging large-scale pre-trained language models.
Co-Authors ABDUL MUNIF Agus Ardiansyah Nh Al Isyadi, Fatah Yasin Aldhyno Yoghatama Diah Priyawati Dian Purworini Divi Galih Prasetyo Putri Divi Galih Prasetyo Putri Fatah Yasin Al Irsyadi Fernandes Sinaga Haryanti, Yanti Hepy Adityarini, Hepy Husni Thamrin Jan Wantoro Lelita Azaria Rahmadiva Maryam Muhammad Fahmi Johan Syah Naidoo, Gedala Mulliah Pratama, Putra Weka Riyanarto Sarno Rona Rizkhy Bunga Chasana Salam, Farah Danisha Saputra, Shafa Bani Setyawan, Sidiq Siti Rochimah Sohail Akhtar Sohail Akhtar Wili Astuti Wiratmoko, Galih Yusuf Sulistyo Nugroho Zebada, Alana Mulya ‘Ammar, Mohammad Faqih Eza