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All Journal Jurnal Algoritma JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Algoritma Journal of Law, Poliitic and Humanities Coreid Journal Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Integrated : Journal Of Information Technology and Vocational Education
Aldy Rialdy Atmadja
UIN Sunan Gunung Djati Bandung
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Pemanfaatan Transformer untuk Peringkasan Teks: Studi Kasus pada Transkripsi Video Pembelajaran Fadlilah, Muhammad Furqon; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6342

Abstract

Abstract−In the digital era, learning videos are increasingly being used, however, they often contain irrelevant information, making it difficult to comprehend the content. This study proposes an approach based on the Whisper and T5 models to generate text summaries from YouTube educational video transcripts. Whisper is used for speech-to-text transcription, focusing on model variants that offer a low Word Error Rate (WER) and time efficiency. Subsequently, the T5 model is fine-tuned to produce accurate text summaries, with a strategy of segmenting the transcript to address input length limitations. Text preprocessing is not applied as it resulted in better evaluation quality. The results show that the combination of Whisper Turbo and the optimized T5 model provides the best performance, with F1-Scores on the ROUGE metrics of 39.23 (ROUGE-1), 13.17 (ROUGE-2), and 23.84 (ROUGE-L). This approach successfully generates more relevant and comprehensive text summaries, enhancing the effectiveness of video-based learning. Therefore, this research makes a significant contribution to the development of text summarization technology for learning videos.
Co-Authors Aaz Muhammad Hafidz Azis Adawiyah, Ayuni Adilla Febrina Agung Wahana Agustin, Ilham Rizky Akmaliyah Akmaliyah Alfi Dawa Mumtaazy Amiq Amiq Angelyna, Angelyna Arkaan, Shabiq Ghazi Asep Rohimat Ayu Puji Rahayu Cecep Nurul Alam, Cecep Nurul Dede Kurniadi Deden Suparman, Deden Diena Rauda Ramdania Dini Destiani Siti Fatimah Dyka Afan Afthori Fadlilah, Muhammad Furqon Firdaus, Muhammad Deden Fridayanti Fridayanti Hani Hamidah Hendar Riyadi Ichsan Budiman Iqbal, Arif Muhamad Juliansyah, Roby Jumadi Jumadi Kartamanah, Fatih Fauzan Leni Fitriani Lupi Krisrupianti Mohamad Irfan Mohamad Irfan, Mohamad Muhamad Farid Padilah Muhamad Fawaz Nurfauzan Muhammad Adam Dzulqarnain Muhammad Adi Nugraha Muhammad Dzulfi Muwaffaq Muhammad Fauzi Rachman Muhammad Luthfi Aziz Nadia Rohimah Nurfikri Habibulloh Padilah, Muhamad Farid Pamungkas, Arba Adhy Pasha, Muhammad Kemal Pebri Alkautsar Ratnasih, Teti Rifan Alamsyah Rifqi Syamsul Fuadi Sipa Almasik Sri Rahayu Sri Sulastri Wildan Budiawan Zulfikar Wisnu Uriawan Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yeni Pariyatin Yeni Pariyatin Zanuar Ekaputra Rus’an