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Relevant bibliographies by topics / IndoBERT

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Academic literature on the topic 'IndoBERT'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "IndoBERT"

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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Conference papers on the topic "IndoBERT"

1

Masaling, Nikita Ananda Putri, Ricky Reynardo Siswanto, and Abba Suganda Girsang. "Indonesian Tweet Emotion Detection Using IndoBERT." In 2024 International Conference on Information Management and Technology (ICIMTech). IEEE, 2024. https://doi.org/10.1109/icimtech63123.2024.10780847.

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Chandra, Kelvin, Kristo Amadeus Prasetya, Rizky Dwi Saputra, and Muhammad Fikri Hasani. "Leveraging IndoBert for CyberBullying Classification on Social Media." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747874.

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Nugroho, Rengga Prakoso, Yerry Soepriyanto, Muhammad Tri Panunggal Aprianto, Aris Triwahyu Febriansah, Muhammad Syifa'ul Qolbi, and Khusnul Khuluq. "Leveraging IndoBERT and Google NLP for Learning Evaluation Tool." In 2024 10th International Conference on Education and Technology (ICET). IEEE, 2024. https://doi.org/10.1109/icet64717.2024.10778447.

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Novandian, Yohanes Deny, Ardytha Luthfiarta, Dhiaka Shabrina Assyifa, et al. "IndoBERT-based Indonesian Cyberbullying Detection with Multi-stage Labeling." In 2024 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2024. https://doi.org/10.1109/isemantic63362.2024.10762553.

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Kurniawan, Marcel, and Budi Juarto. "Unlocking the Potential of IndoBERT for Classification of Indonesian Thesis." In 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2024. https://doi.org/10.1109/icimcis63449.2024.10956541.

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Rihaadatul'Aisy, Nasywaa, and Endang Wahyu Pamungkas. "Sentiment Analysis of Indonesian News Texts Using IndoBERT and IndoRoBERTa." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080939.

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Samosir, Feliks Victor Parningotan, and Steven Riyaldi. "Sentiment Analysis of TikTok Comments on Indonesian Presidential Elections Using IndoBERT." In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024. http://dx.doi.org/10.1109/iccit62134.2024.10701256.

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Qomariyah, Nunung Nurul, Angeline Karen, Vania Natalie, Dimitar Kazakov, and Praharsa Akmaja Chaetajaka. "Evaluating IndoBERT Deep Learning NLP for Disease Classification in Radiology Reports." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963417.

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Aliyah, Nathania Elirica, Rizka Wakhidatus Sholikah, Hafara Firdausi, Henning Titi Ciptaningtyas, and Irzal Ahmad Sabilla. "Enhancing Automated Essay Scoring in Bahasa Indonesia with IndoBERT and IndoSBERT." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080721.

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Millatina Aghnia Fariha, Tsania, and Kemas Lhaksmana. "Indonesian Fake News Classification Using Bi-LSTM With IndoBERT and Data Augmentation." In 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). IEEE, 2025. https://doi.org/10.1109/icadeis65852.2025.10933014.

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