Published: 2025-04-01
Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama
DOI: 10.35870/jtik.v9i2.3535
Downloads
Abstract
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 accuracy. These findings provide strategic insights for the government in understanding public perception regarding the development of Nusantara and demonstrate the potential of Transformer-based sentiment analysis for the Indonesian language. The study recommends further exploration of factors influencing sentiment and analysis on other social media platforms.
Keywords
Independence Day ; IndoBERT ; Nusantara ; Sentiment Analysis ; TextBlob ; Transformer
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 2 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/jtik.v9i2.3535
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Triana Dewi Salma
Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.
Muhammad Ferdi Kurniawan
Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.
Rizqi Darmawan
Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.
Amat Basri
Program Studi Sistem Informasi, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.
-
Fauzianto, R. A. (2022). Analisis Sentimen Opini Masyarakat Terhadap Tech Winter Pada Twitter Menggunakan Natural Language Processing. Jurnal Syntax Admiration, 3(9), 1577-1585. https://doi.org/10.46799/jsa.v3i9.909.
-
Geni, L., Yulianti, E., & Sensuse, D. I. (2023). Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using IndoBERT Language Models. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 9(3), 746-757.
-
Gunawan, K. I., & Santoso, J. (2021). Multilabel text classification menggunakan svm dan doc2vec classification pada dokumen berita bahasa indonesia. Journal of Information System, Graphics, Hospitality and Technology, 3(01), 29-38. https://doi.org/10.37823/insight.v3i01.126.
-
Gupta, A. D., Singh, K., Singh, K. D., Kushwaha, P., Lohani, B. P., & Kumar, S. (2024). Unveiling Insights: Exploring Healthcare Data through Data Analysis. 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), 575–581. https://doi.org/10.1109/IC3SE62002.2024.10593333.
-
Imaduddin, H., A’la, F. Y., & Nugroho, Y. S. (2023). Sentiment analysis in Indonesian healthcare applications using IndoBERT approach. International Journal of Advanced Computer Science and Applications, 14(8).
-
K, G., & P, S. (2022). Evaluating Popular Smartphone Brands Based On Twitter Sentiment Using Textblob. ICTACT Journal on Image and Video Processing, 12(3), 2655–2660. https://doi.org/10.21917/ijivp.2022.0377.
-
Kaharudin, A., Supriyadi, A. A., Baitika, H., & Derryanur, M. (2023). Analisis Sentimen pada Media Sosial dengan Teknik Kecerdasan Buatan Naïve Bayes: Kajian Literatur Review. OKTAL: Jurnal Ilmu Komputer Dan Sains, 2(06), 1642-1649.
-
Kartika, I. D., & Baskara, D. S. (2024, October). Pengembangan Financial Documentation System Berbasis Artificial Intelligence Untuk Meningkatkan Self Service Dan Layanan Prima Bidang Keuangan. In Prosiding Seminar Nasional Sains dan Teknologi Terapan (No. 1).
-
Kaur, C., & Sharma, A. (2020). Twitter sentiment analysis on coronavirus using textblob. EasyChair2516-2314.
-
M, H., & M.N, S. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01–11. https://doi.org/10.5121/ijdkp.2015.5201.
-
Mas Diyasa, I. G. S., Marini Mandenni, N. M. I., Fachrurrozi, M. I., Pradika, S. I., Nur Manab, K. R., & Sasmita, N. R. (2021). Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob. IOP Conference Series: Materials Science and Engineering, 1125(1), 012034. https://doi.org/10.1088/1757-899x/1125/1/012034.
-
Merdiansah, R., Siska, S., & Ridha, A. A. (2024). Analisis sentimen pengguna X Indonesia terkait kendaraan listrik menggunakan IndoBERT. Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI), 7(1), 221-228. https://doi.org/10.55338/jikomsi.v7i1.2895.
-
North, K., Ranasinghe, T., Shardlow, M., & Zampieri, M. (2024). Deep learning approaches to lexical simplification: A survey. Journal of Intelligent Information Systems. https://doi.org/10.1007/s10844-024-00882-9.
-
Putri, A. D., Sholekhah, F., Dadynata, E., Efrizoni, L., Rahmaddeni, R., & Sapina, N. (2024). Penerapan Algoritma Decesion Tree C4.5 untuk Memprediksi Tingkat Kelangsungan Hidup Pasien Kanker Tiroid. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(4), 1485–1495. https://doi.org/10.57152/malcom.v4i4.1532.
-
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre-trained Models for Natural Language Processing: A Survey. https://doi.org/10.1007/s11431-020-1647-3.
-
Rasappan, P., Premkumar, M., Sinha, G., & Chandrasekaran, K. (2024). Transforming sentiment analysis for e-commerce product reviews: Hybrid deep learning model with an innovative term weighting and feature selection. Information Processing & Management, 61(3), 103654. https://doi.org/10.1016/j.ipm.2024.103654.
-
Salma, T. D., Saptawati, G. A. P., & Rusmawati, Y. (2021). Text Classification Using XLNet with Infomap Automatic Labeling Process. 2021 8th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), 1–6. https://doi.org/10.1109/ICAICTA53211.2021.9640255.
-
Sutoyo, E., & Almaarif, A. (2020). Twitter sentiment analysis of the relocation of Indonesia’s capital city. Bulletin of Electrical Engineering and Informatics, 9(4), 1620–1630. https://doi.org/10.11591/eei.v9i4.2352.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright and Licensing Agreement
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
- Authors retain full copyright of their work
- Authors grant the journal right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0)
- This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.