Implementasi Deteksi Judul Berita Clickbait Berbahasa Indonesia dengan pre-trained model Multilingual BERT Pada Aplikasi Berbasis Chrome Extension

Authors

  • Girinoto Girinoto Politeknik Siber dan Sandi Negara
  • Dhana Arvina Alwan Politeknik Siber dan andi Negara
  • Gusti Agung Ngurah Gde K.T. D Politeknik Siber dan Sandi Negara
  • Olga Geby Nabila
  • Arizal arizal Politeknik Siber dan Sandi Negara
  • Dimas Febriyan Priambodo Politeknik Siber dan Sandi Negara http://orcid.org/0000-0001-5347-3248

DOI:

https://doi.org/10.30646/sinus.v20i2.624

Keywords:

Chrome Extension, Clickbait, BERT, Natural Language Processing, Transformers.

Abstract

Clickbait news title is often used by online news portal. The purpose of clickbait is to attract reader to open and read the news. Furthermore, news containing clickbait title can give negative impact by reducing the essence of important news. Therefore, clickbait detection tool is needed to avoid the clickbait news title. Chrome extension was chosen in this study because it supports all Chrome based browsers, such as Google Chrome, Chromium, Microsoft Edge, and Opera so that many users apply this program. In this study, Chrome extension-based application was designed and integrated by using artificial intelligence model. This application also utilized the availability of pre-trained multilingual BERT model as Natural Language Processing (NLP) which will be used to predict a clickbait news title. This study used Multilingual BERT model as NLP because this model has been trained into 104 languages, including Bahasa Indonesia and it has significant performance. The result of this study can detect clickbait news along with 92% of AUC-ROC value.   

Author Biography

Dimas Febriyan Priambodo, Politeknik Siber dan Sandi Negara

Master of Computer Science, 2018 Universitas Gadjah Mada

Dosen Politeknik Siber dan Sandi Negara, 2019

References

Botnevik, B., Sakariassen, E., & Setty, V. (2020. (2020). Brenda: Browser extension for fake news detection. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2117–2120.

Chen, Y., Conroy, N. K., & Rubin, V. L. (2015). News in an online world: The need for an “automatic crap detector.†Proceedings of the Association for Information Science and Technology, 1–4.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.

Dhawan, M., & Ganapathy, V. (2009). Analyzing Information Flow in JavaScript-Based Browser Extensions. 2009 Annual Computer Security Applications Conference, 382–391. https://doi.org/10.1109/ACSAC.2009.43

Fakhruzzaman, M. N., & Gunawan, S. W. (2021). Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT. arXiv.

Fakhruzzaman, M. N., Jannah, S. Z., Ningrum, R. A., & Fahmiyah, I. (2021). Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT). arXiv.

Hadiyat, Y. D. (2019). Clickbait on Indonesia Online Media. Journal Pekommas, 4(1), 1. https://doi.org/10.30818/jpkm.2019.2040101

Handoko, A. P. (2006). Aplikasi Pengolah Bahasa Alami Untuk Operasi Queri Database. Jurnal Ilmiah Sinus, 4(2). https://doi.org/http://dx.doi.org/10.30646/sinus.v4i2

Handoko, A. P. (2007). Aplikasi Pengolah Bahasa Alami Untuk Operasi Boolean Antar Citra. Jurnal Ilmiah Sinus, 5, 2. https://doi.org/http://dx.doi.org/10.30646/sinus.v5i2

Masse, M. (2011). REST API Design Rulebook: Designing Consistent RESTful Web Service Interfaces. O’Reilly Media, Inc.

Narkhede, S. (2018). Understanding AUC - ROC Curve | by Sarang Narkhede | Towards Data Science. Towards Data Science. https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5

Pires, T., Schlinger, E., & Garrette, D. (2019). How multilingual is multilingual BERT? Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4996--5001. https://doi.org/10.18653/v1/P19-1493

William, A., & Sari, Y. (2020). CLICK-ID: A novel dataset for Indonesian clickbait headlines. Data in Brief, 32(10623), 1.

Downloads

Published

2022-07-19

How to Cite

Girinoto, G., Alwan, D. A., Gde K.T. D, G. A. N., Nabila, O. G., arizal, A., & Priambodo, D. F. (2022). Implementasi Deteksi Judul Berita Clickbait Berbahasa Indonesia dengan pre-trained model Multilingual BERT Pada Aplikasi Berbasis Chrome Extension. Jurnal Ilmiah SINUS, 20(2), 25–32. https://doi.org/10.30646/sinus.v20i2.624

Issue

Vol. 20 No. 2 (2022): Vol. 20 No. 2 Juli 2022

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

 

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Similar Articles

> >> 

You may also start an advanced similarity search for this article.