Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama

Authors

  • Triana Dewi Salma Universitas LIA
  • Muhammad Ferdi Kurniawan Universitas LIA
  • Rizqi Darmawan Universitas LIA
  • Amat Basri Universitas LIA

DOI:

https://doi.org/10.35870/jtik.v9i2.3535

Keywords:

Independence Day, IndoBERT, Nusantara, Sentiment Analysis, TextBlob, Transformer

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.

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Author Biographies

  • Triana Dewi Salma, Universitas LIA,

    Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.

  • Muhammad Ferdi Kurniawan, Universitas LIA,

    Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.

  • Rizqi Darmawan, Universitas LIA,

    Program Studi Informatika, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.

  • Amat Basri, Universitas LIA,

    Program Studi Sistem Informasi, Fakultas Sains dan Bisnis, Universitas LIA, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia.

References

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.

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Published

2025-04-01

Issue

Section

Computer & Communication Science

How to Cite

Salma, T. D., Kurniawan, M. F., Darmawan, R., & Basri, A. (2025). Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(2), 757-764. https://doi.org/10.35870/jtik.v9i2.3535

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