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Journal articles on the topic 'Indonesian bidirectional encoder representation of transformers'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Khairu, Nissa Nuzulul, and Evi Yulianti. "Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5641–52. https://doi.org/10.11591/ijece.v13i5.pp5641-5652.

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Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature re

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Nabiilah, Ghinaa Zain, Islam Nur Alam, Eko Setyo Purwanto, and Muhammad Fadlan Hidayat. "Indonesian multilabel classification using IndoBERT embedding and MBERT classification." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 1071. http://dx.doi.org/10.11591/ijece.v14i1.pp1071-1078.

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The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thu

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Nissa, Nuzulul Khairu, and Evi Yulianti. "Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5641. http://dx.doi.org/10.11591/ijece.v13i5.pp5641-5652.

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<p><span lang="EN-US">Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was<br /> pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classif

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Vincentio, Alfonso Darren, and Seng Hansun. "A Fine-Tuned BART Pre-trained Language Model for the Indonesian Question-Answering Task." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21398–403. https://doi.org/10.48084/etasr.9828.

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The information extraction process from a given context can be time consuming and a Pre-trained Language Model (PLM) based on the transformer architecture could reduce the time needed to obtain the information. Moreover, PLM is easily fine-tuned to accomplish certain tasks, one of which is the Question-Answering (QA) task. In literature, QA tasks are generally fine-tuned using encoder-based PLMs, such as the Bidirectional Encoder Representations from Transformers (BERT), where the generated answers come from the extraction process of the context. In order to be able to return more abstract ans

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Lucky, Henry, and Derwin Suhartono. "Investigation of Pre-Trained Bidirectional Encoder Representations from Transformers Checkpoints for Indonesian Abstractive Text Summarization." Journal of Information and Communication Technology 21, No.1 (2021): 71–94. http://dx.doi.org/10.32890/jict2022.21.1.4.

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Text summarization aims to reduce text by removing less useful information to obtain information quickly and precisely. In Indonesian abstractive text summarization, the research mostly focuses on multi-document summarization which methods will not work optimally in single-document summarization. As the public summarization datasets and works in English are focusing on single-document summarization, this study emphasized on Indonesian single-document summarization. Abstractive text summarization studies in English frequently use Bidirectional Encoder Representations from Transformers (BERT), a

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Mas'ud, Abi, Bambang Krismono Triwijoyo, and Dadang Priyanto. "Prediksi Gender Berdasarkan Nama Menggunakan Kombinasi Model IndoBERT, Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (BiLSTM)." JTIM : Jurnal Teknologi Informasi dan Multimedia 7, no. 3 (2025): 448–60. https://doi.org/10.35746/jtim.v7i3.736.

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This study proposes a name-based gender prediction model in the Indonesian language by combining the architectures of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). The non-standardized and diverse structure of Indonesian names presents a significant challenge for text-based gender classification tasks. To address this, a hybrid approach was developed to leverage the contextual representation power of IndoBERT, the local pattern extraction capability of CNN, and the sequential

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Dwiyono, Aswin, Abdiansah Abdiansah, and Muhammad Fachrurrozi. "Analisis Perbandingan Klasifikasi Intent Chatbot Menggunakan Deep Learning BERT, RoBERTa, dan IndoBERT." Journal of Information System Research (JOSH) 6, no. 1 (2024): 595–606. https://doi.org/10.47065/josh.v6i1.6051.

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A chatbot is a software application to designed handle user inputs and generate appropriate replies based on those inputs, which are then communicated back to the user. In able to provide accurate responses, the chatbot must be able to understand the intent of the user accurately. An issue in the development of chatbots is how to accurate classify user intent. Incorrectly understanding user intent can result in irrelevant responses. In order to have a conversation with the user, the intent of the user needs to be classified correctly. This paper compares three state-of-the-art transformer-base

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Wibawa, Aji Prasetya, Denis Eka Cahyani, Didik Dwi Prasetya, Langlang Gumilar, and Andrew Nafalski. "Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 7137. http://dx.doi.org/10.11591/ijece.v13i6.pp7137-7146.

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<span>One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural networ

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Diyah Utami Kusumaning Putri and Dinar Nugroho Pratomo. "Clickbait Detection of Indonesian News Headlines using Fine-Tune Bidirectional Encoder Representations from Transformers (BERT)." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 7, no. 2 (2022): 162–68. http://dx.doi.org/10.25139/inform.v7i2.4686.

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The problem of the existence of news article that does not match with content, called clickbait, has seriously interfered readers from getting the information they expect. The number of clickbait news continues significantly increased in recent years. According to this problem, a clickbait detector is required to automatically identify news article headlines that include clickbait and non-clickbait. Additionally, many currently existing solutions use handcrafted features and traditional machine learning methods, which limit the generalization. Therefore, this study fine-tunes the Bidirectional

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Yulita, Intan Nurma, Victor Wijaya, Rudi Rosadi, Indra Sarathan, Yusa Djuyandi, and Anton Satria Prabuwono. "Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)." Data 8, no. 3 (2023): 46. http://dx.doi.org/10.3390/data8030046.

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To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people’s social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from tran

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Jaya, Alwi. "Analisis Sentimen Pandangan Public Profesi PNS (Pegawai Negeri Sipil) dari Twiter menerapkan indonesian Roberta Base Sentiment Classifier." Indonesian Journal of Data and Science 4, no. 1 (2023): 38–44. http://dx.doi.org/10.56705/ijodas.v4i1.66.

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Penelitian ini dilakukan untuk mendapatkan opini masyarakat terkait profesi Pegawai Negeri Sipil (PNS) yang beredar di media sosial twitter dengan menggunakan metode algoritma Bidirectional Encoder Representations from Transformers (BERT) yang di mana dalam Bahasa Indonesia berarti representasi dua arah encoder. BERT berguna untuk mengolah representasi dua arah yang berada dalam teks tanpa nama dengan menggabungkan sisi kanan dan kiri pada sebuah konteks dalam segala bagian.yang di gunakan untuk menentukan positif, negatif atau neutralnya pandangan masyarakat terkait profesi PNS yang beredar d

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Oswari, Teddy, Murniyati Murniyati, Trityanti Yusnitasari, Nurasiah Nurasiah, and Seviyanti Wijay. "Sentiment Analysis of Indonesian Youtube Reviews About Lesbian, Guy, Bisexual and Transgender (LGBT) using IndoBERT Fine Tuning." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 15, no. 1 (2024): 26. http://dx.doi.org/10.24843//lkjiti.2024.v15.i01.p03.

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Lesbian, gay, Bisexual, and Transgender (LGBT) is an individual who has a sexual orientation or gender identity that is different from the heterosexual majority. The LGBT community now dares to appear openly on social media; nowadays, social media is used as a source of information and a place to provide comments. The Indonesian state generally still views the LGBT community as deviant behavior. This research was conducted to understand Indonesian society's views on LGBT through YouTube and social media. The text mining method analyzes and classifies the counter or pro sentences expressed in t

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Wicaksono, Tri Buwono Bagus, and Rama Dian Syah. "IMPLEMENTASI METODE BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS UNTUK ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI ACCESS." Jurnal Ilmiah Informatika Komputer 29, no. 3 (2024): 254–65. https://doi.org/10.35760/ik.2024.v29i3.12514.

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Technological developments in this digital era are growing rapidly in various fields, one of which is the field of public transportation. The purpose of this study is to conduct a sentiment analysis of Access by KAI application users on the Google Play Store so that it can be used as a suggestion to improve the quality of the application. This paper uses the Bidirectional Encoding Representations from Transformers (BERT) method with the pretrained IndoBERT model to train the Indonesian dataset. This writing method uses the CRISP-DM method with 6 stages, namely Business Understanding, Data Unde

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Situmeang, Samuel, Sarah Rosdiana Tambunan, Lidia Ginting, Wahyu Krisdangolyanti Simamora, and Winda Sari ButarButar. "Indonesian automated short-answer grading using transformers-based semantic similarity." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 3 (2025): 1034. https://doi.org/10.11591/ijict.v14i3.pp1034-1043.

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Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered f

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Muhammad, Noor Fakhruzzaman, and Wildan Gunawan Sie. "CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1232–38. https://doi.org/10.5281/zenodo.7042214.

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This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internetconnected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often clutt

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Yulianti, Evi, Naradhipa Bhary, Jafar Abdurrohman, Fariz Wahyuzan Dwitilas, Eka Qadri Nuranti, and Husna Sarirah Husin. "Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5489. http://dx.doi.org/10.11591/ijece.v14i5.pp5489-5501.

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The large volume of court decision documents in Indonesia poses a challenge for researchers to assist legal practitioners in extracting useful information from the documents. This information can also benefit the general public by improving legal transparency, law enforcement, and people's understanding of the law implementation in Indonesia. A natural language processing task that extracts important information from a document is called named entity recognition (NER). In this study, the NER task is applied to legal domains, which is then referred to as legal entity recognition (LER) task. In

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Fakhruzzaman, Muhammad Noor, and Sie Wildan Gunawan. "CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1232. http://dx.doi.org/10.11591/ijai.v11.i4.pp1232-1238.

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This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internet-connected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often clut

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Haerani, Erna, Alam Rahmatulloh, and Souhayla Elmeftahi. "Bidirectional Encoder Representations from Transformers Fine-Tuning for Sentiment Classification of Cek Bansos Reviews." International Journal of Engineering and Computer Science Applications (IJECSA) 4, no. 1 (2025): 59–70. https://doi.org/10.30812/ijecsa.v4i1.4981.

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Social assistance programs are essential government initiatives aimed at supporting underprivileged communities. One such program is facilitated through the Cek Bansos application, which enables users to check their eligibility for social aid. However, user experiences with the application vary, leading to a range of sentiments in their reviews. Understanding these sentiments is crucial for improving the application’s functionality and user satisfaction. This study focuses on sentiment analysis of user reviews of the Cek Bansos application by leveraging a fine-tuned Indonesian-language Bidirec

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Guna Mandhasiya, Dwi, Hendri Murfi, and Alhadi Bustamam. "The hybrid of BERT and deep learning models for Indonesian sentiment analysis." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 591. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp591-602.

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<span>Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pat

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Mandhasiya, Dwi Guna, Hendri Murfi, and Alhadi Bustamam. "The hybrid of BERT and deep learning models for Indonesian sentiment analysis." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 591–602. https://doi.org/10.11591/ijeecs.v33.i1.pp591-602.

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Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recogni

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Roesmiatun Purnamadewi, Yasinta, and Amalia Zahra. "Enhancing detection of zero-day phishing email attacks in the Indonesian language using deep learning algorithms." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 505–12. http://dx.doi.org/10.11591/eei.v14i1.8759.

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Email phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patte

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Muhammad, Noor Fakhruzzaman, Zahrotul Jannah Sa'idah, Ardiati Ningrum Ratih, and Fahmiyah Indah. "Flagging clickbait in Indonesian online news websites using finetuned transformers." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2921–30. https://doi.org/10.11591/ijece.v13i3.pp2921-2930.

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Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding

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Asri, Yessy, Dwina Kuswardani, Amanda Atika Sari, and Atikah Rifdah Ansyari. "Word embedding for contextual similarity using cosine similarity." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1170. https://doi.org/10.11591/ijeecs.v38.i2.pp1170-1180.

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Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated da

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Yessy, Asri Dwina Kuswardani Amanda Atika Sari Atikah Rifdah Ansyari. "Word embedding for contextual similarity using cosine similarity." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1170–80. https://doi.org/10.11591/ijeecs.v38.i2.pp1170-1180.

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Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated da

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Fakhruzzaman, Muhammad Noor, Sa'idah Zahrotul Jannah, Ratih Ardiati Ningrum, and Indah Fahmiyah. "Flagging clickbait in Indonesian online news websites using fine-tuned transformers." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2921. http://dx.doi.org/10.11591/ijece.v13i3.pp2921-2930.

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Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using hyperbolic and interesting words, sometimes unfinished sentences in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model multilingual bidirectional encoder representations from transformers (BERT) that acted as an embedding laye

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Wiwin, Suwarningsih, Suwarningsih Wiwin, Yusuf Rahadika Fadhil, and Havid Albar Purnomo Mochamad. "RoBERTa: language modelling in building Indonesian question-answering systems." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 6 (2022): 1248–55. https://doi.org/10.12928/telkomnika.v20i6.24248.

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This research aimed to evaluate the performance of the A Lite BERT (ALBERT), efficiently learning an encoder that classifies token replacements accurately (ELECTRA) and a robust optimized BERT pretraining approach (RoBERTa) models to support the development of the Indonesian language question and answer system model. The evaluation carried out used Indonesian, Malay and Esperanto. Here, Esperanto was used as a comparison of Indonesian because it is international, which does not belong to any person or country and this then make it neutral. Compared to other foreign languages, the structure and

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Ariyanto, Amelia Devi Putri, Fari Katul Fikriah, and Arif Fitra Setyawan. "Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform." Pixel :Jurnal Ilmiah Komputer Grafis 17, no. 1 (2024): 179–85. http://dx.doi.org/10.51903/pixel.v17i1.2010.

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The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings suc

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Alifi, Muhammad Riza, Djoko Cahyo Utomo Lieharyani, Bima Putra Sudimulya, and Mohammad Rizky Maulidhan. "Implementation of IndoNLU Pre-Trained Model for Aspect-Based Sentiment Analysis of Indonesian Stock News." JURNAL TEKNIK INFORMATIKA 16, no. 2 (2023): 151–60. http://dx.doi.org/10.15408/jti.v16i2.33791.

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Investors in Indonesia are increasing from year to year, especially mutual fund investors managed by investment managers. News is one of the factors considered by investment managers in making stock investment decisions. Very diverse news sources and different writing styles can make it difficult to retrieve information on each issuer in the news. In this research, the aspect-based sentiment analysis (ABSA) method is implemented to extract news specifically on each aspect (issuer) in the news and evaluate the issuer. The model used is a pre-trained Indonesian Bidirectional Encoder Representati

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Simanjuntak, Lihardo Faisal, Rahmad Mahendra, and Evi Yulianti. "We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model." Big Data and Cognitive Computing 6, no. 3 (2022): 77. http://dx.doi.org/10.3390/bdcc6030077.

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Twitter user location data provide essential information that can be used for various purposes. However, user location is not easy to identify because many profiles omit this information, or users enter data that do not correspond to their actual locations. Several related works attempted to predict location on English-language tweets. In this study, we attempted to predict the location of Indonesian tweets. We utilized machine learning approaches, i.e., long-short term memory (LSTM) and bidirectional encoder representations from transformers (BERT) to infer Twitter users’ home locations using

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Yulianti, Evi, and Nuzulul Khairu Nissa. "ABSA of Indonesian customer reviews using IndoBERT: single- sentence and sentence-pair classification approaches." Bulletin of Electrical Engineering and Informatics 13, no. 5 (2024): 3579–89. http://dx.doi.org/10.11591/eei.v13i5.8032.

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Aspect-based sentiment analysis (ABSA) task is important to identify user satisfaction from customer reviews by recognizing the sentiments of all aspects discussed in the reviews. This work investigates a novel study on the effectiveness and efficiency of three IndoBERT-based models for solving the ABSA task in Indonesian language. IndoBERT is a state-of-the-art transformer-based model, i.e., bidirectional encoder representations from transformers (BERT), that was pre-trained on Indonesian language. Our first model utilizes IndoBERT in a feature-based mode, paired with the convolutional neural

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Wijaya, Darryl Rayhan, Gusti Made Arya Sasmitha, and Wayan Oger Vihikan. "Sentiment Analysis of Indonesian Citizens on Electric Vehicle Using FastText and BERT Method." Journal of Information Systems and Informatics 6, no. 3 (2024): 1360–72. http://dx.doi.org/10.51519/journalisi.v6i3.784.

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Electric vehicles have become one of the most important innovations in the automotive industry in recent years. This is not only related to technological developments, but also to its significant impact on the environment and lifestyle of global society. Lot of people do not know about the benefit of using electric vehicles for our environment. The transition from conventional vehicles to electric vehicles can really make our environment healthier and also reducing the pollution. At the same time, debates and feelings about electric vehicles continue to grow around the world. This study aims t

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Widansyah, Muhammad, Fathia Frazna Az-Zahra, and Agung Pambudi. "Fine-Tuning Model Indobert (Indonesian Bidirectional Encoder Representations from Transformers) untuk Analisis Sentimen Berbasis Aspek pada Aplikasi M-Paspor." Joutica 9, no. 2 (2024): 183–95. https://doi.org/10.30736/informatika.v9i2.1310.

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M-paspor adalah aplikasi untuk membantu masyarakat dalam proses permohonan paspor. Di Google Play Store aplikasi ini telah diunduh lebih dari 1 juta pengguna dengan ulasan yang diperoleh sebanyak 29 ribu. Data ulasan ini dapat dimanfaatkan untuk mengetahui kelebihan dan kekurangan aplikasi berdasarkan pengalaman nyata. Dengan Teknik analisis sentiemen berbasis aspek, ulasan pengguna dapat dimanfaatkan untuk mengevaluasi aplikasi dan pengembangan aplikasi. Sehingga kualitas layanan aplikasi dapat meningkat. Penelitian ini bertujuan untuk menganalisis sentimen pada ulasan dengan mengelompokannya

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Vincent, Vincent, and Amalia Zahra. "Multilingual hate speech detection using deep learning." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 3 (2025): 1015. https://doi.org/10.11591/ijict.v14i3.pp1015-1023.

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The rise of social media has enabled public expression but also fueled the spread of hate speech, contributing to social tensions and potential violence. Natural language processing (NLP), particularly text classification, has become essential for detecting hate speech. This study develops a hate speech detection model on Twitter using FastText with bidirectional long short-term memory (Bi-LSTM) and explores multilingual bidirectional encoder representations from transformers (M-BERT) for handling diverse languages. Data augmentation techniques-including easy data augmentation (EDA) methods, b

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Suwarningsih, Wiwin, and Nuryani Nuryani. "Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 819–29. https://doi.org/10.11591/ijece.v14i1.pp819-829.

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Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The

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Fitra Ramadhan, Zekri, and Achmad Benny Mutiara. "Sentiment Analysis of Honkai: Star Rail Indonesian Language Reviews on Google Play Store Using Bidirectional Encoder Representations from Transformers Method." International Journal of Engineering, Science and Information Technology 3, no. 3 (2023): 1–6. http://dx.doi.org/10.52088/ijesty.v3i3.462.

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Online games are a type of entertainment that is done by humans to have fun and forget all the problems in everyday life. Honkai: Star Rail is a new online game application owned by miHoYo which is currently popular and widely downloaded on the Google Play Store. Reviews on the Honkai: Star Rail app are increasing over time so this makes it difficult for app developers to know past user reviews on their apps. Therefore, the author conducted a study to analyze sentiment towards Honkai: Star Rail application reviews in Indonesian on the Google Play Store using the Bidirectional Encoder Represent

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Suwarningsih, Wiwin, and Nuryani Nuryani. "Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 819. http://dx.doi.org/10.11591/ijece.v14i1.pp819-829.

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Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The reaso

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Syaifudin, Mohamad Fahmi, Gagatsatya Adiatmaja, and Bilal Hidayaturrohman. "Calculation of Similarity between MUI Fatwas: A Comparison of Text Extraction Features and String Matching Algorithms." Halal Research Journal 5, no. 1 (2025): 1–13. https://doi.org/10.12962/j22759970.v5i1.1226.

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Fatwas, as religious rulings issued by the Indonesian Ulama Council (MUI), play a crucial role in guiding the Muslim community. This research aims to analyze the similarity between these fatwas, contributing to the field by comparing various similarity methods. The dataset includes 380 fatwa titles collected from the official website of the National Sharia Council of the Indonesian Ulama Council. The research follows a structured methodology: starting with data collection, followed by text pre-processing involving punctuation removal, stemming, and stop word elimination. Word extraction techni

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Harjo, Budi, Muljono Muljono, and Rachmad Abdullah. "Homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English machine translation." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 7036. http://dx.doi.org/10.11591/ijece.v14i6.pp7036-7045.

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Homonym and polysemy features can influence some errors in translation from a source language to another target language, for example, from Indonesian to English. A lemma or a morphology factor can cause the configuration of Indonesian homonym features. For example, the word beruang can mean an animal beruang (bear) and can mean a verb alternation ber+uang (has/have money). The Indonesian polysemy feature can also impact an error in the translation process because it can have a literal meaning and a symbolic meaning. For example, the terms bunga melati (jasmine flower) and bunga hati (lover),

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Prisscilya, Veren, and Abba Suganda Girsang. "Classification of Indonesia False News Detection Using Bertopic and Indobert." Jurnal Indonesia Sosial Teknologi 5, no. 8 (2024): 3061–79. http://dx.doi.org/10.59141/jist.v5i8.1310.

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In the current global era, the development of technology and information is very rapid, so it is very easy to get information/news from the internet. Because of the ease of getting this information, there is a lot of circulating fake news (hoaxes), the news is not filtered so anyone can spread news that is not clear in content. This can lower a person's credibility in the professional world, cause division, threaten physical and mental health, and can also result in material losses. Based on this, to stop the spread of hoaxes is to detect them as early as possible and block them. This detectio

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Tedjasulaksana, Jeffrey Junior, and Abba Suganda Girsang. "Virality classification from Twitter data using pre-trained language model and multi-layer perceptron." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1952. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1952-1962.

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Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanc

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Jeffrey, Junior Tedjasulaksana Abba Suganda Girsang. "Virality classification from Twitter data using pre-trained language model and multi-layer perceptron." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1952–62. https://doi.org/10.11591/ijeecs.v35.i3.pp1952-1962.

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Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanc

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Yazid, Ahmad Subhan, and Edi Winarko. "Fine-Tuning BERT untuk Menangani Ambiguitas Pada POS Tagging Bahasa Indonesia." Jurnal Linguistik Komputasional (JLK) 6, no. 2 (2023): 57–64. http://dx.doi.org/10.26418/jlk.v6i2.148.

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Ambiguitas merupakan masalah yang kerap muncul pada tugas-tugas pemrosesan bahasa alami, termasuk pada POS tagging (pelabelan kelas kata). Penelitian ini bertujuan menangani ambiguitas pada POS tagging bahasa Indonesia dengan pendekatan pembelajaran mendalam BERT (bidirectional encoder representation from transformers). Pendekatan ini dipilih untuk melengkapi menambah fleskfibilitas dari penelitian sebelumnya yang menerapkan metode berbasis aturan dan probabilistik. Untuk mendapatkan model yang optimal dan dapat menyelesaikan ambiguitas, dilakukan beberapa eksperimen dengan skenario fine-tunin

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Said, Fadillah, and Lindung Parningotan Manik. "Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning." Paradigma - Jurnal Komputer dan Informatika 24, no. 2 (2022): 160–67. http://dx.doi.org/10.31294/paradigma.v24i2.1415.

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Pemilihan presiden tahun 2019 merupakan pemilihan presiden yang menjadi perbincangan hangat selama beberapa waktu bahkan orang membicarakan topik ini sejak tahun 2018 di internet. Dalam memprediksi pemenang pemilihan presiden penelitian sebelumnya telah melakukan penelitian terhadap dataset Analisis sentimen berbasis aspek (ABSA) pemilihan presiden tahun 2019 menggunakan algoritma pembelajaran mesin seperti Support Vector Machine (SVM), Naive Bayes (NB), dan K-Nearest Neighbors (KNN) dan menghasilkan akurasi yang cukup baik. Penelitian ini mengusulkan metode deep learning dengan menggunakan mo

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Adib Ulinuha El Majid and Reflan Nuari. "Performance Comparison Of BERT Metrics and Classical Machine Learning Models (SVM,Naive Bayes) for Sentiment Analysis." INOVTEK Polbeng - Seri Informatika 10, no. 2 (2025): 741–52. https://doi.org/10.35314/wmh3rg23.

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Sentiment analysis is one of the important methods in understanding public opinion from large amounts of text, such as product reviews or user comments. Many studies have shown that the BERT (BiDirectional Encoder Representations from Transformers) model has advantages over classical machine learning models such as Support Vector Machine (SVM) and Naïve Bayes. However, there are still few studies that systematically compare the performance of the two on datasets from various topics and languages, especially those with imbalanced label distributions. This study compares four BERT variants (bert

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Latifah, Nurun, Ramaditia Dwiyansaputra, and Gibran Satya Nugraha. "Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 3 (2024): 663–76. http://dx.doi.org/10.30812/matrik.v23i3.3835.

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The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SM

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Yefferson, Danny Yongky, Viriyaputra Lawijaya, and Abba Suganda Girsang. "Hybrid model: IndoBERT and long short-term memory for detecting Indonesian hoax news." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1913. http://dx.doi.org/10.11591/ijai.v13.i2.pp1913-1924.

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The world has entered an era that technology has developed far. Due to rapid technological development, information is easily spread. However, not all information spread through social media is factual information. Responding to this social phenomenon, we initiated to create a hoax detection system using the combined method of Indo bidirectional encoder representations from transformers (IndoBERT) and long short-term memory (LSTM). The dataset used in this study are obtained through the process scraping on the site turnbackhoax.id and cable news network (CNN) Indonesia. We decided to use the I

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Danny, Yongky Yefferson, Lawijaya Viriyaputra, and Suganda Girsang Abba. "Hybrid model: IndoBERT and long short-term memory for detecting Indonesian hoax news." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1913–24. https://doi.org/10.11591/ijai.v13.i2.pp1913-1924.

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The world has entered an era that technology has developed far. Due to rapid technological development, information is easily spread. However, not all information spread through social media is factual information. Responding to this social phenomenon, we initiated to create a hoax detection system using the combined method of Indo bidirectional encoder representations from transformers (IndoBERT) and long short-term memory (LSTM). The dataset used in this study are obtained through the process scraping on the site turnbackhoax.id and cable news network (CNN) Indonesia. We decided to use the I

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Asri, Yessy, Dwina Kuswardani, Widya Nita Suliyanti, Yosef Owen Manullang, and Atikah Rifdah Ansyari. "Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 677. https://doi.org/10.11591/ijeecs.v38.i1.pp677-688.

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PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the applica

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Yessy, Asri Dwina Kuswardani Widya Nita Suliyanti Yosef Owen Manullang Atikah Rifdah Ansyari. "Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 677–88. https://doi.org/10.11591/ijeecs.v38.i1.pp677-688.

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  PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the

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Khotimah, Purnomo Husnul, Andria Arisal, Andri Fachrur Rozie, et al. "Monitoring Indonesian online news for COVID-19 event detection using deep learning." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 957. http://dx.doi.org/10.11591/ijece.v13i1.pp957-971.

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Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 mon

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