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Journal articles on the topic 'IndoBERT embedding'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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2

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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3

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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Mannix, Ilma Alpha, and Evi Yulianti. "Academic expert finding using BERT pre-trained language model." International Journal of Advances in Intelligent Informatics 10, no. 2 (2024): 280. http://dx.doi.org/10.26555/ijain.v10i2.1497.

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Academic expert finding has numerous advantages, such as: finding paper-reviewers, research collaboration, enhancing knowledge transfer, etc. Especially, for research collaboration, researchers tend to seek collaborators who share similar backgrounds or with the same native languages. Despite its importance, academic expert findings remain relatively unexplored within the context of Indonesian language. Recent studies have primarily relied on static word embedding techniques such as Word2Vec to match documents with relevant expertise areas. However, Word2Vec is unable to capture the varying me

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5

Kamal, Ahmad, and Renita Astri. "Eksplorasi Sentimen Pengguna pada Aplikasi E-Commerce dengan Deep Learning." Jurnal Teknologi Dan Sistem Informasi Bisnis 7, no. 3 (2025): 435–41. https://doi.org/10.47233/jteksis.v7i3.2010.

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Penelitian ini menganalisis sentimen pengguna terhadap aplikasi e‑commerce terkemuka di Indonesia melalui klasifikasi teks berbasis pembelajaran mendalam. Sebanyak 50.000 ulasan berbahasa Indonesia dikumpulkan secara berimbang dari Google Play dan App Store untuk Tokopedia, Shopee, Bukalapak, Lazada, dan Blibli. Dua pendekatan mutakhir diterapkan—jaringan Long Short‑Term Memory (LSTM) dengan embedding FastText pralatih dan Bidirectional Encoder Representations from Transformers (IndoBERT v2) yang disesuaikan. Pra‑proses data mencakup pembersihan teks, normalisasi slang, stemming, dan tokenisas

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6

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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7

Imron, Syaiful, Esther Irawati Setiawan, and Joan Santoso. "Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN." Journal of Intelligent System and Computation 5, no. 1 (2023): 10–16. http://dx.doi.org/10.52985/insyst.v5i1.267.

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Dengan semakin berkembangnya teknologi informasi, maka muncul istilah e-commerce dalam dunia bisnis. Pada e-commerce ada fitur review, pelanggan dapat memberikan review berupa teks, gambar, dan bintang. Review tersebut merupakan opini dari pelanggan terkait barang yang dibeli. Tetapi pada kebanyakan e-commerce tidak ada fitur kategori terkait review hal ini membuat calon pembeli kesusahan dalam menganalisa secara manual. Aspect-based sentiment analysis (ABSA) merupakan solusi dari permasalahan tersebut. ABSA memiliki tiga tugas salah satunya Aspect Category Detection yang memiliki fungsi untuk

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8

Bhagaskara S M, Pernanda Arya, Sri Suryani Prasetiyowati, and Yuliant Sibaroni. "Hoax Detection of Indonesian News Media on Twitter Using IndoBERT with Word Embedding Word2Vec." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (2023): 1088. http://dx.doi.org/10.30865/mib.v7i3.6367.

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Hoax is data that is added or deducted from the news that occurred. In the digital age, hoaxes are increasingly being spread, and people are very quickly affected by their spread, especially hoaxes circulating in Indonesian news media on social media. Disseminating information that has not been confirmed as accurate can cause public concern and anxiety. Virtual diversion has transformed into a correspondence key to begin thinking, talking, and moving around cordial issues. In this manner, exploration will be led by consolidating the IndoBERT model with the Word2Vec development highlight in arr

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9

Ariyatma, Rama Dona, and Bagus Priambodo. "Analysis of Performance Labelling Sentiment Between K-Means Indobert And Inset Lexicon-Based." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 10, no. 1 (2025): 58. https://doi.org/10.30645/jurasik.v10i1.849.

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Sentiment analysis, a natural language processing technique, plays a key role in identifying opinions or sentiments from textual data. Accurate sentiment labelling within a dataset significantly impacts the performance of sentiment analysis models. However, manual labelling can be time-consuming. Many researchers utilize lexicon-based methods for sentiment labelling, but lexicons are often limited in reflecting topic-specific nuances, potentially leading to inaccurate sentiment representation. This inaccuracy can negatively affect classification models. Inset Lexicon (Indonesia Sentiment Lexic

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10

Pranata, Joni, Surya Agustian, Jasril Jasril, and Elin Haerani. "Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1668–76. https://doi.org/10.47065/bits.v6i3.6335.

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Masalah keterbatasan data latih menjadi tantangan utama dalam klasifikasi sentimen di berbagai bahasa, termasuk bahasa Indonesia, terutama untuk analisis sentimen terkait topik tertentu. Hal ini disebabkan oleh berbagai faktor, dan umumnya adalah kebutuhan untuk mengetahui dengan segera bagaimana sentimen terhadap suatu isu, sehingga tidak mungkin menghabiskan waktu untuk memberi label yang cukup pada data untuk proses pelatihan. Penelitian ini mengusulkan model klasifikasi sentimen dengan sumber data pelatihan yang sedikit, pada studi kasus pengangkatan Kaesang Pangarep sebagai ketua umum PSI

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11

Kusnawi, Kusnawi, and Khoerul Anam. "Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 803–18. https://doi.org/10.52436/1.jutif.2025.6.2.4015.

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The results of the 2024 Indonesian presidential election decided that Prabowo Subianto and Gibran Rakabuming Raka became the elected pair of Indonesian presidential and vice-presidential candidates in 2024. The pair's election triggered various public reactions, especially on social media platforms. Some social media platforms provided diverse opinions, indicating a wide variety of views on this issue. This research aims to analyze public opinion after the election of the 2024 Indonesian president by comparing sentiment using TextBlob, VADER (Valence Aware Dictionary and sEntiment Reasoner), a

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12

Cahyadi, Marcelinus Fajar, and Theresia Herlina Rochadiani. "Implementasi Ensemble Deep Learning Untuk Analisis Sentimen Terhadap Genre Game Mobile." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 3 (2024): 1512. http://dx.doi.org/10.30865/mib.v8i3.7832.

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The rapid growth of the online gaming industry in Indonesia has prompted developers to address various challenges in creating successful mobile games. This study aims to evaluate the effectiveness of ensemble learning techniques, particularly soft voting, in enhancing sentiment analysis accuracy across 17 genres of mobile games. Additionally, it identifies the most effective deep learning model for sentiment classification. The research compares the performance of CNN-LSTM, BERT, and CNN-GRU models, as well as an ensemble of these models. Review data was collected from the Google Play Store, t

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13

Priwibowo, Aqge, Chandra Kusuma Dewa, and Ahmad Luthfi. "Enhancing Disease Diagnosis Coding: A Deep Learning Approach with Bidirectional GRU For ICD-10 Classification." JURNAL INFOTEL 17, no. 2 (2025): 299–319. https://doi.org/10.20895/infotel.v17i2.1320.

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The health insurance claim in hospitals involves selecting specific ICD-10 codes for primary diagnosis texts. With rising claim volumes, the need for faster, more accurate coding is critical. This study develops a deep learning model to classify diagnosis texts into relevant ICD-10 codes using 9,982 original medical records from a national referral hospital under the Indonesian Ministry of Health. The classification method employs a BiGRU layer architecture, known for its effectiveness in handling sequential data, such as diagnosis texts. BiGRU operates bidirectionally, enhancing the model’s a

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14

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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15

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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16

Hakim, Valianda Farradillah, and Dwiza Riana. "Analysis of User Complaints for Telecommunication Brands on X (Twitter) using IndoBERT and Deep Learning." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 13, no. 2 (2024): 270–79. http://dx.doi.org/10.23887/janapati.v13i2.76497.

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Tweeting on different official accounts is what users of Twitter (X) do most frequently. These tweets ranging from compliments to critiques. One of the official accounts that gets a lot of tweets from its customers is Telkomsel, an Indonesian telecom company. This study aims to find the maximum accuracy that can be obtained by combining CNN and Bi-LSTM algorithms with IndoBERT embeddings. A considerable accuracy level above 90% is demonstrated by the study, with CNN obtaining the greatest accuracy of 99% at a learning rate of 6*10^-5, along with scores of 98%, 97%, and 97% for precision, recal

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17

Irmawan, Oky Ade, Indra Budi, Aris Budi Santoso, and Prabu Kresna Putra. "Improving Sentiment Analysis and Topic Extraction in Indonesian Travel App Reviews Through BERT Fine-Tuning." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 13, no. 2 (2024): 359–70. http://dx.doi.org/10.23887/janapati.v13i2.77028.

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Abstract The increasing use of the internet in Indonesia has an influence on the presence of Online Travel Agents (OTA). Through the OTA application, users can book transportation and accommodation tickets more easily and quickly. The increasingly rigorous competition is causing companies like PT XYZ to be able to provide solutions to the needs and problems of their customers in the field of online ticket booking. Many customers submit reviews of the use of the PT XYZ application through Playstore and Appstore, and it needs a technique to group thousands of reviews and detect the topics discus

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18

Arkaan, Shabiq Ghazi, Aldy Rialdy Atmadja, and Muhammad Deden Firdaus. "Fake News Detection in the 2024 Indonesian General Election Using Bidirectional Long Short-Term Memory (BI-LSTM) Algorithm." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 21, no. 2 (2024): 22–30. http://dx.doi.org/10.33751/komputasi.v21i2.9987.

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The advancement of information technology provides convenience, but it also brings about problems. One area affected by this is the election process in Indonesia, which has seen a rise in fake news often used to discredit political opponents. Fake news misleads the public into believing incorrect information related to the election. To address this issue, a system is needed to detect fake news in the 2024 election to help the public differentiate between true and false information. This system is developed using an artificial intelligence and deep learning approach trained to do text classific

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19

Ghinaa, Zain Nabiilah, Nur Alam Islam, Setyo Purwanto Eko, and Fadlan Hidayat Muhammad. "Indonesian multilabel classification using IndoBERT embedding and MBERT classification." 14, no. 1 (2024). https://doi.org/10.11591/ijece.v14i1.pp1071-1078.

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The rapid increase in social media activity has triggered various discussionspaces and information exchanges on social media. Social media users caneasily tell stories or comment on many things without limits. However, thisoften triggers open debates that lead to fights on social media. This isbecause many social media users use toxic comments that contain elementsof racism, radicalism, pornography, or slander to argue and cornerindividuals or groups. These comments can easily spread and trigger usersvulnerable to mental disorders due to unhealthy and unfair debates on socialmedia. Thus, a mod

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20

Hamdikatama, Bimantyoso. "BEYOND ALGORITHMS: AN INTEGRATED APPROACH TO FAKE NEWS DETECTION USING MACHINE LEARNING TECHNIQUES." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 3 (2025). https://doi.org/10.33480/jitk.v10i3.6061.

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The internet has become a major source of information, but it also facilitates the rapid spread of fake news, which can significantly influence public opinion and social decisions. While various techniques have been developed for detecting fake news, many studies focus on individual algorithms, which often result in suboptimal performance. This study addresses this gap by comparing machine learning models, including Support Vector Classification (SVC), XGBoost, and a Stacking Ensemble that combines both SVC and XGBoost, to determine the most effective approach for fake news detection. Text pre

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