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Journal articles on the topic 'IndoBERT'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Hafiza, Annisaa Alya, and Erwin Budi Setiawan. "Enhancing Cyberbullying Detection on Platform 'X' Using IndoBERT and Hybrid CNN-LSTM Model." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 655–72. https://doi.org/10.52436/1.jutif.2025.6.2.4321.

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Cyberbullying on social media platforms has become widespread in society. Cyberbullying can take many forms, including hate speech, trolling, adult content, racism, harassment, or rants. One social media platform that has many cyberbullies is Twitter, which has been renamed 'X'. The anonymous nature of this 'X' platform allows users from all over the world to commit cyberbullying as they can freely share their thoughts and expressions without having to account for their identity. This research aims to explore the influence of IndoBERT’s semantic features on hybrid deep learning models for cybe

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Ridho, Muhammad Yusuf, and Evi Yulianti. "From Text to Truth: Leveraging IndoBERT and Machine Learning Models for Hoax Detection in Indonesian News." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 10, no. 3 (2024): 544–55. https://doi.org/10.26555/jiteki.v10i3.29450.

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In the era of technology and information exchange online content being deceitful poses a serious threat to public trust and social harmony on a global scale. Detective mechanisms to identify content are essential for safeguard the populace effectively. This study is dedicated to creating a machine learning system that can automatically spot deceptive content in Indonesian language by utilizing IndoBERT. A model specifically tailored for the intricacies of the Indonesian language. IndoBERT was selected due to its capacity to grasp the linguistic nuances present, in Indonesian text which are oft

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Tobing, Charlotte Jocelynne L., IGN Lanang Wijayakusuma, and Luh Putu Ida Harini. "Perbandingan Kinerja IndoBERT dan MBERT Untuk Deteksi Berita Hoaks Politik dalam Bahasa Indonesia." JST (Jurnal Sains dan Teknologi) 14, no. 1 (2025): 114–23. https://doi.org/10.23887/jstundiksha.v14i1.92126.

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Berita hoaks telah menjadi tantangan serius dalam era digital, terutama dalam ranah politik di Indonesia. Penelitian ini membandingkan kinerja IndoBERT dan Multilingual BERT (MBERT) dalam tugas deteksi berita hoaks berbahasa Indonesia. Dataset yang digunakan terdiri dari berita politik dengan label fakta dan hoaks. Model IndoBERT dan MBERT dilatih menggunakan skema fine-tuning dengan evaluasi berbasis metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil eksperimen menunjukkan bahwa IndoBERT unggul dalam mendeteksi berita hoaks dibandingkan dengan MBERT, terutama karena IndoBERT telah

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Wicaksono, Galih Wasis, Sheila Fitria Al asqalani, Yufis Azhar, Nur Putri Hidayah, and Andreawana Andreawana. "Automatic Summarization of Court Decision Documents over Narcotic Cases Using BERT." JOIV : International Journal on Informatics Visualization 7, no. 2 (2023): 416. http://dx.doi.org/10.30630/joiv.7.2.1811.

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Reviewing court decision documents for references in handling similar cases can be time-consuming. From this perspective, we need a system that can allow the summarization of court decision documents to enable adequate information extraction. This study used 50 court decision documents taken from the official website of the Supreme Court of the Republic of Indonesia, with the cases raised being Narcotics and Psychotropics. The court decision document dataset was divided into two types, court decision documents with the identity of the defendant and court decision documents without the defendan

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Putra, Thariq Iskandar Zulkarnain Maulana, Suprapto Suprapto, and Arif Farhan Bukhori. "Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia." Jurnal Ilmu Siber dan Teknologi Digital 1, no. 1 (2022): 1–28. http://dx.doi.org/10.35912/jisted.v1i1.1509.

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Purpose: This research aims to improve the performance of the text classification model from previous studies, by combining the IndoBERT pre-trained model with the Long Short-Term Memory (LSTM) architecture in classifying Indonesian-language tweets into several categories. Method: The classification text based on multiclass classification was used in this research, combined with pre-trained IndoBERT namely Long Short-Term Memory (LTSM). The dataset was taken using crawling method from API Twitter. Then, it will be compared with Word2Vec-LTSM and fined-tuned IndoBERT. Result: The IndoBERT-LSTM

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Bagestra, Ricky, Alim Misbullah, Zulfan Zulfan, Rasudin Rasudin, Laina Farsiah, and Sri Azizah Nazhifah. "Performance Assessment of Machine Learning and Transformer Models for Indonesian Multi-Label Hate Speech Detection." Infolitika Journal of Data Science 2, no. 2 (2024): 62–71. https://doi.org/10.60084/ijds.v2i2.235.

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Hate speech, characterized by language that incites discrimination, hostility, or violence against individuals or groups based on attributes such as race, religion, or gender, has become a critical issue on social media platforms. In Indonesia, unique linguistic complexities, such as slang, informal expressions, and code-switching, complicate its detection. This study evaluates the performance of Support Vector Machine (SVM), Naive Bayes, and IndoBERT models for multi-label hate speech detection on a dataset of 13,169 annotated Indonesian tweets. The results show that IndoBERT outperforms SVM

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Salma, Triana Dewi, Muhammad Ferdi Kurniawan, Rizqi Darmawan, and Amat Basri. "Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 9, no. 2 (2025): 757–64. https://doi.org/10.35870/jtik.v9i2.3535.

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The inaugural Indonesian Independence Day celebration in the new capital, Nusantara, marked a historic milestone. This study analyzes public sentiment toward this event using the IndoBERT model. Data was collected from Twitter during the celebration period and classified into positive, negative, and neutral sentiments. Three main approaches were employed: IndoBERT as a baseline, IndoBERT fine-tuned with IndoNLU data, and IndoBERT applied to TextBlob-labeled data. Results indicate that the TextBlob-IndoBERT model outperforms the others, effectively processing informal Indonesian text with high

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Taufiq Dwi Purnomo and Joko Sutopo. "COMPARISON OF PRE-TRAINED BERT-BASED TRANSFORMER MODELS FOR REGIONAL LANGUAGE TEXT SENTIMENT ANALYSIS IN INDONESIA." International Journal Science and Technology 3, no. 3 (2024): 11–21. http://dx.doi.org/10.56127/ijst.v3i3.1739.

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This study compared the performance of eight pre-trained BERT-based models for sentiment analysis across ten regional languages in Indonesia. The objective was to identify the most effective model for analyzing sentiment in low-resource Indonesian languages, given the increasing need for automated sentiment analysis tools. The study utilized the NusaX dataset and evaluated the performance of IndoBERT (IndoNLU), IndoBERT (IndoLEM), Multilingual BERT, and NusaBERT, each in both base and large variants. Model performance was assessed using the F1-score metric. The results indicated that models pr

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Wildan Amru Hidayat and Vinna Rahmayanti Setyaning Nastiti. "PERBANDINGAN KINERJA PRE-TRAINED INDOBERT-BASE DAN INDOBERT-LITE PADA KLASIFIKASI SENTIMEN ULASAN TIKTOK TOKOPEDIA SELLER CENTER DENGAN MODEL INDOBERT." JSiI (Jurnal Sistem Informasi) 11, no. 2 (2024): 13–20. http://dx.doi.org/10.30656/jsii.v11i2.9168.

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Era digital telah membawa revolusi dalam dunia e-commerce dengan mengintegrasikan platform media sosial dan platform e-commerce, yang menghasilkan inovasi seperti aplikasi TikTok Tokopedia Seller Center. Aplikasi ini menggabungkan platform e-commerce dengan fitur media sosial, memungkinkan pengguna untuk mengelola penjualan sekaligus memperluas jangkauan pasar dan mempromosikan produk melalui video pendek yang interaktif pada platform media sosial TikTok. Dengan adanya inovasi fitur baru dalam aplikasi ini, penelitian ini melakukan analisis sentimen untuk memahami persepsi dan ulasan berbahasa

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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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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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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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Arnanda, Feza Raff, and Aisyah 'Azizah Nur Rahmah. "Deteksi Pergerakan IHSG Berdasarkan Berita Daring Menggunakan Model Deep Learning Berbasis Transformer." Seminar Nasional Official Statistics 2024, no. 1 (2024): 439–48. http://dx.doi.org/10.34123/semnasoffstat.v2024i1.1951.

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Transformasi ekonomi Indonesia menuntut adanya industrialisasi yang didukung oleh pasar modal sebagai katalisator. Pasar modal berperan penting dalam perekonomian melalui informasi multisumber seperti berita daring yang berpengaruh terhadap pergerakan IHSG. Penelitian ini bertujuan untuk mendeteksi pergerakan IHSG berdasarkan berita daring menggunakan model transformer, yaitu indoBERT-base, indoBERT-large, indoBERT-lite-large, dan multilingual-BERT. Data dikumpulkan melalui web scraping dari laman detik.com periode Agustus 2018 hingga Mei 2024. Model machine learning seperti Random Forest, Sup

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Jayadianti, Herlina, Wilis Kaswidjanti, Agung Tri Utomo, Shoffan Saifullah, Felix Andika Dwiyanto, and Rafal Drezewski. "Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN." ILKOM Jurnal Ilmiah 14, no. 3 (2022): 348–54. http://dx.doi.org/10.33096/ilkom.v14i3.1505.348-354.

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Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to au

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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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Khairani, Ulfia, Viska Mutiawani, and Hendri Ahmadian. "Pengaruh Tahapan Preprocessing Terhadap Model Indobert Dan Indobertweet Untuk Mendeteksi Emosi Pada Komentar Akun Berita Instagram." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (2024): 887–94. http://dx.doi.org/10.25126/jtiik.1148315.

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Platform media sosial seperti Instagram telah membentuk ruang di mana berita dapat dengan mudah ditemukan dan menarik perhatian individu. Pada Instagram, dapat memberikan komentar-komentar terhadap berita yang telah dibaca. Pemahaman terhadap emosi yang mengiringi komentar-komentar yang telah diberikan pengguna pada postingan berita dapat membantu memahami bagaimana berita tersebut diserap, diinterpretasi, dan direspons oleh publik. Penelitian ini mengkategorikan empat emosi yaitu marah, senang, takut, dan sedih dengan menggunakan model terlatih IndoBERT dan IndoBERTweet. Penelitian ini bertuj

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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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Muhammad, Alwan Naufal, and Suganda Girsang Abba. "Traffic accident classification using IndoBERT." International Journal of Informatics and Communication Technology 13, no. 1 (2024): 42–49. https://doi.org/10.11591/ijict.v13i1.pp42-49.

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Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compar

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Naufal, Muhammad Alwan, and Abba Suganda Girsang. "Traffic accident classification using IndoBERT." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 1 (2024): 42. http://dx.doi.org/10.11591/ijict.v13i1.pp42-49.

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<span>Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performanc

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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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Indah Rahajeng, Mahanti, and Ayu Purwarianti. "Indonesian Question Answering System for Factoid Questions using Face Beauty Products Knowledge Graph." Jurnal Linguistik Komputasional (JLK) 4, no. 2 (2021): 59. http://dx.doi.org/10.26418/jlk.v4i2.62.

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Question answering (QA) system is developed to find the right answers from natural language questions. QA systems can be used for building chatbots or even search engines. In this study, we’ve built an Indonesian QA system that uses Anindya Knowledge Graph as its data source. The idea behind this QA system is translating questions into SPARQL queries. The proposed solution consists of four modules, namely question classification, information extraction, token mapping, and query construction. The question classification and the information extraction modules were experimented using SVM, LSTM, a

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Widyawan, Widyawan, Bayu Prasetiyo Utomo, and Muhammad Nur Rizala. "A Novel Fusion of Machine Learning Methods for Enhancing Named Entity Recognition in Indonesian Language Text." Jurnal Sistem Informasi Bisnis 14, no. 4 (2024): 311–20. http://dx.doi.org/10.21456/vol14iss4pp311-320.

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One of the important implementations in machine learning is Named Entity Recognition (NER), which is used to process text and extract entities such as people, organizations, laws, religions, and locations. NER for the Indonesian language still faces significant challenges due to the lack of high-quality labelled datasets, which limits the development of more advanced models. To address this issue, we utilized several pre-trained BERT models (bert-base-uncased, indobenchmark/indobert-base-p1, indolem/indobert-base-uncased) and datasets (NERGRIT-IndoNLU, NERGRIT-Corpus, NERUGM, and NERUI). This

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Aras, Suhardi, Muhammad Yusuf, Reinhard Yohanis Ruimassa, Elli Agustinus Billi Wambrauw, and Elsa Bura Pala'langan. "Sentiment Analysis on Shopee Product Reviews Using IndoBERT." Journal of Information Systems and Informatics 6, no. 3 (2024): 1616–27. http://dx.doi.org/10.51519/journalisi.v6i3.814.

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A marketplace is a place in cyberspace where there are commercial activities between buyers and sellers. Products offered from the marketplace have reviews to review. Shopee is the most visited marketplace by people and offers various products. Product reviews can provide benefits for other consumers in assessing the products offered. By utilizing NLP technology in particular, this study can classify positive sentiment and negative sentiment in product review data. The IndoBERT model is a model that can be used in NLP technology by utilizing the relationship between each input and output eleme

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Prabowo, Aziz, and Fadil Indra Sanjaya. "Penerapan Metode Transfer Learning Pada Indobert Untuk Analisis Sentimen Teks Bahasa Jawa Ngoko Lugu." Simkom 9, no. 2 (2024): 205–17. http://dx.doi.org/10.51717/simkom.v9i2.478.

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Peningkatan signifikan dalam penggunaan media sosial dan platform online di Indonesia telah menyebabkan lonjakan produksi dan penyebaran teks dalam berbagai bahasa daerah, termasuk Bahasa Jawa. Hal ini menciptakan kebutuhan mendesak akan sistem analisis sentimen yang dapat memahami nuansa Bahasa Jawa dalam konteks opini dan ekspresi pengguna. Penelitian ini bertujuan untuk menerapkan metode transfer learning pada model IndoBERT, dengan fokus pada evaluasi kinerjanya dalam tugas analisis sentimen teks Bahasa Jawa Ngoko Lugu. Hasil penelitian menunjukkan bahwa model IndoBERT mencapai akurasi, pr

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Baharuddin, Fikri, and Mohammad Farid Naufal. "Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy." Journal of Information Systems Engineering and Business Intelligence 9, no. 2 (2023): 253–63. http://dx.doi.org/10.20473/jisebi.9.2.253-263.

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Background: The learning assessment of elementary schools has recently incorporated Bloom's Taxonomy, a structure in education that categorizes different levels of cognitive learning and thinking skills, as a fundamental framework. This assessment now includes High Order Thinking Skill (HOTS) questions, with a specific focus on Indonesian topics. The implementation of this system has been observed to require teachers to manually categorize or classify questions, and this process typically requires more time and resources. To address the associated difficulty, automated categorization and class

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Baihaqi, Wiga Maulana, and Arif Munandar. "Sentiment Analysis of Student Comment on the College Performance Evaluation Questionnaire Using Naïve Bayes and IndoBERT." JUITA : Jurnal Informatika 11, no. 2 (2023): 213. http://dx.doi.org/10.30595/juita.v11i2.17336.

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The development of the Internet has played a significant role in various aspects of life and has generated vast amounts of data, including student comments about universities. The challenge in analyzing comment data is the large number of students providing feedback, which makes manual analysis impractical. The purpose of this study is to analyze the performance evaluation of universities by students in terms of positive and negative sentiments, with the aim of assessing the level of student satisfaction with all elements and areas of university operations. This research utilized the Naïve Bay

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Arief Rahman Hakim and Alva Hendi Muhammad. "PERBANDINGAN MODEL TRANSFORMER, DEEP LEARNING, DAN MACHINE LEARNING UNTUK DETEKSI BERITA PALSU: STUDI KASUS PADA TEKS BERBAHASA INDONESIA." Jurnal Manajemen Informatika dan Sistem Informasi 8, no. 2 (2025): 188–97. https://doi.org/10.36595/misi.v8i2.1591.

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Deteksi berita palsu dalam bahasa Indonesia masih menjadi tantangan dalam pemrosesan bahasa alami (NLP). Penelitian ini membandingkan enam metode: RoBERTa, BERT, IndoBERT, SVM, LSTM, dan CNN dalam mengidentifikasi berita palsu. Dataset yang digunakan telah melalui proses pembersihan dan tokenisasi sebelum diterapkan pada masing-masing model. Penelitian ini memberikan analisis komprehensif terhadap keunggulan model Transformer dibandingkan dengan metode klasik seperti SVM, CNN, dan LSTM. Selain itu, penelitian ini juga menegaskan bahwa model yang dilatih khusus untuk bahasa Indonesia, seperti I

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Geni, Lenggo, Evi Yulianti, and Dana Indra Sensuse. "Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 746–57. https://doi.org/10.26555/jiteki.v9i3.26490.

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General election is one of the crucial moments for a democratic country, e.g., Indonesia. Good election preparation can increase people's participation in the general election. In this study, we conduct a sentiment analysis of Indonesian public opinion on the upcoming 2024 election using Twitter data and IndoBERT model. This study is aimed at helping the government and related institutions to understand public perception. Therefore, they could obtain valuable insights to better prepare for elections, including evaluating the election policies, developing campaign strategies, increasing voter e

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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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Manoppo, Michael Reynald, Indri Claudia Kolang, Daffa Nur Nur Fiat, et al. "ANALISIS SENTIMEN PUBLIK DI MEDIA SOSIAL TERHADAP KENAIKAN PPN 12% DI INDONESIA MENGGUNAKAN INDOBERT." Jurnal Kecerdasan Buatan dan Teknologi Informasi 4, no. 2 (2025): 152–63. https://doi.org/10.69916/jkbti.v4i2.322.

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Penelitian ini menganalisis sentimen publik terkait rencana kenaikan Pajak Pertambahan Nilai (PPN) 12% di Indonesia menggunakan model transformer berbasis Bahasa Indonesia, IndoBERT. Dengan mengumpulkan 2.581 sampel data dari platform media sosial X, Instagram, dan TikTok, penelitian ini BERTujuan untuk memahami respons publik secara mendalam. Data melalui tahapan pra-pemrosesan, tokenisasi, dan label mapping sebelum dibagi 80/10/10 menjadi set pelatihan, validasi, dan pengujian. Model IndoBERT dasar yang di-fine-tuned selama tiga epoch menunjukkan kinerja yang signifikan pada set pengujian. S

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Jocelynne, Charlotte, IGN Lanang Wijayakusuma, and Luh Putu Ida Harini. "Detection of Political Hoax News Using Fine-Tuning IndoBERT." Journal of Applied Informatics and Computing 9, no. 2 (2025): 354–60. https://doi.org/10.30871/jaic.v9i2.8989.

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Indonesia has experienced a surge in the spread of political hoax news, posing a potential threat to democratic and social stability. This study aims to develop a model for detecting political hoax news in the Indonesian language using IndoBERT, a language model optimized for Indonesian text. The dataset was sourced from Kaggle and comprises 20,928 factual news articles and 2,251 hoax news articles from major Indonesian media outlets, including CNN, Kompas, Tempo, and Turnbackhoax. The imbalance between factual and hoax news articles was addressed through undersampling, resulting in 1,302 samp

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Riyadi, Slamet, Lathifah Khansa Salsabila, Cahya Damarjati, and Rohana Abdul Karim. "Sentiment Analysis of YouTube Users on Blackpink Kpop Group Using IndoBERT." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 8, no. 2 (2024): 233–45. http://dx.doi.org/10.29407/intensif.v8i2.22678.

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Background: The Korean Pop (K-Pop) phenomenon has become an important part of popular culture worldwide, with Blackpink being one of the most influential groups. Analyzing sentiment toward Blackpink is urgent, given its growing popularity and wide influence among fans worldwide. In the present technological era, social media platforms such as YouTube have evolved into a space where artists and their fans may interact with each other. As a consequence, social media has become a powerful tool for assessing the emotional tone and sentiment conveyed by individuals. Objective: This research aims to

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Rais Kumar, Abdul Ghofur, Yudi Sukmono, and Aji Ery Burhandenny. "COMPARISON OF SUPPORT VECTOR MACHINE AND INDOBERT IN NON-FUNCTIONAL REQUIREMENT CLASSIFICATION OF APPLICATION USER REVIEWS." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 1035–42. https://doi.org/10.52436/1.jutif.2024.5.4.1424.

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User reviews of mobile applications have become a valuable source of information for evaluating the quality of an application. It is crucial for application developers to understand what users express in their reviews. One aspect that can be analyzed from user reviews is Non-Functional Requirement (NFR). Classifying reviews based on NFR is essential in understanding how an application can be enhanced. Although user reviews have the potential to provide valuable insights into NFR, manually processing thousands of user reviews is a laborious and inefficient task. Therefore, artificial intelligen

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Perwira, Rifki Indra, Vynska Amalia Permadi, Dian Indri Purnamasari, and Riza Prapascatama Agusdin. "Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content." Journal of Information Systems Engineering and Business Intelligence 11, no. 1 (2025): 30–40. https://doi.org/10.20473/jisebi.11.1.30-40.

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Background: Aspect-based sentiment analysis (ABSA) is essential in extracting meaningful insights from user-generated content (UGC) in various domains. In tourism, UGC such as Google Reviews offers essential feedback, but the challenges associated with processing in Indonesian language, including the unique linguistic characteristics, pose difficulties for automatic sentiment, and aspect detection. Recent advancements in transformer-based models, such as BERT, have shown great potential in addressing these challenges by providing context-aware embeddings. Objective: This research aimed to fine

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Anugerah, Sri Mulyani, Rifki Wijaya, and Moch Arif Bijaksana. "Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT." INTEK: Jurnal Penelitian 11, no. 1 (2024): 51. http://dx.doi.org/10.31963/intek.v11i1.4771.

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The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and

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Nuryadi, Delvin, Farindika Metandi, Noor Alam Hadiwijaya, et al. "FINE TUNING INDOBERT UNTUK ANALISIS SENTIMEN PADA ULASAN PENGGUNA APLIKASI TIKET.COM DI GOOGLE PLAY STORE." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 3577–83. https://doi.org/10.36040/jati.v9i2.13204.

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Tiket.com merupakan salah satu platform pemesanan tiket perjalanan terbesar di Indonesia. Ulasan pengguna di Google Play Store dapat memberikan wawasan penting mengenai kepuasan pengguna terhadap layanan aplikasi ini. Namun, analisis manual terhadap ribuan ulasan tidak efisien, sehingga diperlukan pendekatan berbasis machine learning untuk mengklasifikasikan sentimen secara otomatis. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Tiket.com dengan menggunakan model IndoBERT yang telah di fine-tune. Metode yang digunakan meliputi pengumpulan data ulasan melalui web scraping

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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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Amin, Muhammad Basil Musyaffa, Gibran Hakim, Muhammad Taufik Maulana, et al. "Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 6 (2024): 1291–302. https://doi.org/10.25126/jtiik.1168121.

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Spam pada SMS dan Email menyebabkan pengalaman kurang menyenangkan bagi pengguna dalam pemanfaatan teknologi. Spam secara umum merupakan sebuah tindakan mengirim pesan yang tidak diinginkan atau tidak diminta kepada sejumlah besar orang. Spam kini dapat ditemui dalam berbagai bentuk, seperti web maupun multimedia. Penelitian ini bertujuan untuk mengevaluasi model berbasis BERT, khususnya IndoBERT dan MultilingualBERT, dalam mendeteksi dan mengklasifikasi spam berbahasa Indonesia pada pesan SMS dan Email. Model yang dipilih kemudian dilatih untuk mengidentifikasi perbedaan antara pesan spam dan

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Muhammad Ikram Kaer Sinapoy, Yuliant Sibaroni, and Sri Suryani Prasetyowati. "Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 3 (2023): 657–62. http://dx.doi.org/10.29207/resti.v7i3.4830.

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In recent years, social media users have been increasing significantly, in January 2022 social media users in Indonesia reached 191 million people which has an increase of 12.35% from the previous year as many as 170 million people, With this massive increase every year, more and more people tend to seek and consume information through social media. Despite the many advantages provided by social media, However, the quality of information on social media is lower than in traditional news media there is a lot of hoax information spreading. With many disadvantages felt by hoax information, it has

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Amin, Muhammad Basil Musyaffa, Gibran Hakim, Muhammad Taufik Maulana, et al. "Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 6 (2024): 1291–302. https://doi.org/10.25126/jtiik.2024118121.

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Spam pada SMS dan Email menyebabkan pengalaman kurang menyenangkan bagi pengguna dalam pemanfaatan teknologi. Spam secara umum merupakan sebuah tindakan mengirim pesan yang tidak diinginkan atau tidak diminta kepada sejumlah besar orang. Spam kini dapat ditemui dalam berbagai bentuk, seperti web maupun multimedia. Penelitian ini bertujuan untuk mengevaluasi model berbasis BERT, khususnya IndoBERT dan MultilingualBERT, dalam mendeteksi dan mengklasifikasi spam berbahasa Indonesia pada pesan SMS dan Email. Model yang dipilih kemudian dilatih untuk mengidentifikasi perbedaan antara pesan spam dan

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Praha, Tohpatti Crippa, Widodo Widodo, and Murien Nugraheni. "Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1213. http://dx.doi.org/10.62527/joiv.8.2.2126.

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Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM m

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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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Puspitarini, Ni Made Dwipadini, Yuliant Sibaroni, and Sri Suryani Prasetiyowati. "Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 267. http://dx.doi.org/10.30865/mib.v7i1.5439.

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A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities b

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Putra, Agung, Ismarmiaty, and Apriani. "Pengukuran Tingkat Akurasi Pada Ulasan E-Commerce Menggunakan Metode INDOBERT Dengan Optimizer Adam." Jurnal Komputer, Informasi dan Teknologi 5, no. 1 (2025): 14. https://doi.org/10.53697/jkomitek.v5i1.2448.

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Pesatnya perkembangan e-commerce di Indonesia telah menyebabkan meningkatnya volume ulasan pengguna, yang kini menjadi sumber informasi penting dalam memahami kepuasan dan pengalaman pelanggan. Ulasan tersebut mengandung sentimen yang dapat dimanfaatkan untuk pengambilan keputusan bisnis, khususnya melalui pendekatan analisis sentimen. Penelitian ini bertujuan untuk mengukur tingkat akurasi model IndoBERT yang dioptimasi menggunakan algoritma Adam dalam mengklasifikasikan sentimen ulasan e-commerce berbahasa Indonesia. Dataset yang digunakan berasal dari platform Kaggle, terdiri atas 11.606 ul

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Hidayat, Ilham Rizki, and Warih Maharani. "General Depression Detection Analysis Using IndoBERT Method." International Journal on Information and Communication Technology (IJoICT) 8, no. 1 (2022): 41–51. http://dx.doi.org/10.21108/ijoict.v8i1.634.

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Many of the tweets we discover on Twitter are concerning feelings of depression which will be caused by varied things. The amount of tweets additionally continues to increase. To be able to decide however depressed a user is, analysing tweets from users can facilitate with that. The method of analysing the detection of depression can help to supply applicable treatment for users who are detected to own depression. During this paper, the users to be analysed are users who have more than 1000 tweets and are Indonesian tweets. Then, crawling / retrieval of user tweet data is carried out. After th

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Oktariansyah, Indro Abri, Fajri Rakhmat Umbara, and Fatan Kasyidi. "Klasifikasi Sentimen Untuk Mengetahui Kecenderungan Politik Pengguna X Pada Calon Presiden Indonesia 2024 Menggunakan Metode IndoBert." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 636–48. https://doi.org/10.47065/bits.v6i2.5435.

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X has evolved into one of the most popular social media platforms in the world. In Indonesia, the use of X is quite widespread, especially in discussions about the presidential election, which is currently a hot topic. Everyone has different views on the candidates, both positive and negative. With a large amount of tweet data from users, this information can serve as a data source for processing and analysis. Various methods can be used to analyze and classify sentiment from this data, one of which is using BERT. This research conducts sentiment classification using BERT with the IndoBert mod

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Adilla, Rahmi Elfa, Muhammad Huda, Muhammad Aziz, and Lya Hulliyyatus Suadaa. "Aspect-Based Sentiment Analysis of Transportation Electrification Opinions on YouTube Comment Data." Jurnal Aplikasi Statistika & Komputasi Statistik 16, no. 2 (2024): 140–57. https://doi.org/10.34123/jurnalasks.v16i2.790.

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Introduction/Main Objectives: This research aims to conduct an aspect-based sentiment analysis of transportation electrification opinions on YouTube comment data. Background Problems: It is difficult to summarize the sentiment of many YouTube user comments related to electric vehicles (EVs) based on their aspects; therefore, aspect-based sentiment analysis is needed to conduct further analysis. Novelty: This study identifies five aspects of EV and their sentiments at the same time. The aspects are usefulness, ease of use, comfort, cost, and incentive policies. One of this study’s methods is th

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Nurhasiyah, Nurhasiyah, Ramaditia Dwiyansaputra, Santi Ika Murpratiwi, and Arik Aranta. "ANALISIS SENTIMEN PENGGUNA PLATFORM MEDIA SOSIAL X PADA TOPIK PEMILIHAN PRESIDEN 2024 MENGGUNAKAN PERBANDINGAN MODEL MONOLINGUAL DAN MULTILINGUAL BERT." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 1 (2024): 626–34. https://doi.org/10.36040/jati.v9i1.12430.

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Pemilihan Presiden 2024 di Indonesia merupakan topik penting yang banyak dibahas di media sosial, terutama platform X (sebelumnya Twitter). Media sosial ini menyediakan ruang bagi jutaan pengguna untuk berbagi opini yang dapat diolah menjadi data sentiment. Namun, menganalisis opini dalam jumlah besar membutuhkan model yang tepat. Penelitian ini bertujuan untuk menganalisis sentimen publik terkait topik tersebut menggunakan perbandingan model monolingual (IndoBERT) dan multilingual (mBERT), model digunakan untuk mengklasifikasikan sentiment positif, netral, dan negatif. Penelitian dilakukan de

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