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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) eProceedings of Engineering
Ananda Affan Fattahila
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Siti Saadah; Kaenova Mahendra Auditama; Ananda Affan Fattahila; Fendi Irfan Amorokhman; Annisa Aditsania; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.978 KB) | DOI: 10.29207/resti.v6i4.4215

Abstract

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.
Peringkasan Artikel Berita Menggunakan Pendekatan Abstraktif Dengan Model Transformers Ananda Affan Fattahila; Ade Romadhony; Said Al Faraby
eProceedings of Engineering Vol 10, No 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak— Membaca artikel berita merupakan kebiasan rutin bagi mayoritas orang, di mana biasanya digunakan sebagai bahan bacaan atau referensi untuk mendapatkan informasi terbaru yang sedang beredar. Salah satu bentuk atau cara mendapatkan informasi secara cepat dapat dipermudah dengan keberadaan ringkasan artikel berita. Peringkasan artikel berita juga dapat meminimalkan informasi yang berlebihan. Tujuan Tugas Akhir ini adalah menghasilkan ringkasan dengan kualitas yang lebih baik pada beberapa topik berita yang performansinya rendah karena keterbatasan data. Pendekatan peringkasan yang digunakan adalah metode abstraktif dengan memanfaatkan pre-trained model berbasis transformers. Pre-trained model yang digunakan adalah T5-small dan BART-BASE serta untuk meningkatkan performansi, diterapkan proses augmentasi data. Pengujian pada Tugas Akhir ini dilakukan pada dataset XLSum Multi News, yang berisi kategori government & politic, health, economic, infrastructure development, crime dan technology industry dengan crime dan economic mempunyai kualitas ringkasan paling rendah. Evaluasi terhadap hasil ringkasan dilakukan secara otomatis dengan metriks ROUGE dan penilaian secara subyektif dari responden. Hasil evaluasi menunjukkan bahwa dari skor ROUGE tidak terjadi peningkatan performansi, namun responden menyimpulkan bahwa dari sisi relevansi, koherensi, dan kesesuaian, ringkasan yang dihasilkan setelah dilakukan augmentasi data mempunyai kualitas yang lebih baik.Kata Kunci — Berita, Artikel, Peringkasan, Abstraktif, Augmentasi Data, Transformers
Co-Authors Ade Romadhony Al Faraby, Said Aniq Atiqi Rohmawati Annisa Aditsania Fendi Irfan Amorokhman Kaenova Mahendra Auditama Siti Saadah