Analisis Sentimen Publik terhadap Pengaruh Kecerdasan Buatan pada Produksi Animasi Berbasis Data Media Sosial X Menggunakan Metode Lexicon-Based VADER

Authors

DOI:

https://doi.org/10.29240/arcitech.v6i1.16391

Keywords:

Sentiment Analysis, Animation Production, Lexicon-Based VADER Method, Artificial Intelligence Impact, Social Media Analytics

Abstract

Artificial Intelligence (AI) has emerged as a primary driver in various industries, yet its integration into animation production faces mixed reactions, creating a dualism between innovation and professional sustainability. Despite growing research on AI's technical efficiency in animation, there remains a significant gap in understanding public sentiment toward its broader impacts, particularly through large-scale social media analysis. This study aims to address this gap by analyzing public sentiment regarding the impact of AI on animation production. Using the Lexicon-Based VADER method, this study collected and analyzed 1,545 tweets from the social media platform X (formerly Twitter) during the period from June 1 to 30, 2025. The results showed a dominance of positive sentiment with 935 tweets, 385 neutral tweets, and 225 negative tweets. The results of this study are expected to provide insights, reference for decision-making, and further exploration. These findings not only highlight widespread support for AI but also underscore the need for ethical frameworks to mitigate job displacement risks, offering actionable implications for industry stakeholders to foster balanced human-AI collaboration in creative fields.

Downloads

Download data is not yet available.

References

Alex David, S., Begum, A., Ruth Naveena, N., Hemalatha, D., Ravi Kumar, S., & Vijayalakshmi, V. (2024). Navigating Guest Sentiments: Comparative Analysis of Decision Tree and VADER for Hotel Review Analysis. 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 1244–1248. https://doi.org/10.1109/ICSSAS64001.2024.10760783

Amaliah, F., & Dwi Nuryana, I. K. (2022). Perbandingan Akurasi Metode Lexicon Based Dan Naive Bayes Classifier Pada Analisis Sentimen Pendapat Masyarakat Terhadap Aplikasi Investasi Pada Media Twitter. Journal of Informatics and Computer Science (JINACS), 3(03), 384–393. https://doi.org/10.26740/jinacs.v3n03.p384-393

Asri, Y., Suliyanti, W. N., Kuswardani, D., & Fajri, M. (2022). Pelabelan Otomatis Lexicon Vader dan Klasifikasi Naive Bayes dalam menganalisis sentimen data ulasan PLN Mobile. PETIR, 15(2), 264–275. https://doi.org/10.33322/petir.v15i2.1733

Chen, Y., Wang, Y., Yu, T., & Pan, Y. (2024). The Effect of AI on Animation Production Efficiency: An Empirical Investigation Through the Network Data Envelopment Analysis. Electronics, 13(24), 5001. https://doi.org/10.3390/electronics13245001

CILGIN, C., BAS, M., BILGEHAN, H., and UNAL, C. (2022). Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e: Academic Journal of Information Technology, 13(49), 72-89. https://doi.org/10.5824/ajite.2022.02.001.x

Crocamo, C., Viviani, M., Famiglini, L., Bartoli, F., Pasi, G., and Carra, G. (2021). Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis. European Psychiatry, 64(1). https://doi.org/10.1192/j.eurpsy.2021.3

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28). https://doi.org/10.1126/sciadv.adn5290

Gandy, L. M., Ivanitskaya, L. V, Bacon, L. L., & Bizri-Baryak, R. (2025a). Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods. JMIR Formative Research, 9, e57395. https://doi.org/10.2196/57395

Gandy, L. M., Ivanitskaya, L. V, Bacon, L. L., & Bizri-Baryak, R. (2025b). Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods. JMIR Formative Research, 9, e57395. https://doi.org/10.2196/57395

Hernandez-Perez, R., Lara-Martinez, P., Obregon-Quintana, B., Liebovitch, L. S., and Guzman-Vargas, L. (2024). Correlations and Fractality in Sentence-Level Sentiment Analysis Based on VADER for Literary Texts. Information, 15(11), 698. https://doi.org/10.3390/info15110698

Hu, D., Choi, M., Giri, N., Mousas, C., & Adamo-Villani, N. (2025). Perceptions of AI in Animation Production (pp. 82–93). https://doi.org/10.1007/978-3-031-90167-6_6

Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550

In, S., & Chanchamnan, S. (2026). Comparison of VADER and TextBlob labeling for sentiment analysis using machine learning and deep learning models: A study on generative AI user experience. Acta Psychologica, 263, 106268. https://doi.org/10.1016/j.actpsy.2026.106268

Isnan, M., Elwirehardja, G. N., & Pardamean, B. (2023). Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model. Procedia Computer Science, 227, 168–175. https://doi.org/10.1016/j.procs.2023.10.514

Jannan, M. F. Z., & Dwi Putra Negara, Y. (2024). Analisis Sentimen Masyarakat Terhadap Penerima Beasiswa Kartu Indonesia Pintar Kuliah Dengan Metode Support Vector Machine. Journal of Information and Technology, 4(2), 26–30. https://doi.org/10.32938/jitu.v4i2.7598

Jingyang, C. (2024). Research on the Application Status and Pros and Cons of AI in Animation Production. The Frontiers of Society, Science and Technology, 6(11). https://doi.org/10.25236/FSST.2024.061109

Luthfi Krisna Bayu, & Wurijanto, T. (2024). Analisis sentimen mobil listrik menggunakan metode Naïve Bayes Classifier. INFOTECH : Jurnal Informatika & Teknologi, 5(2), 328–335. https://doi.org/10.37373/infotech.v5i2.1465

McGuire, J., De Cremer, D., & Van de Cruys, T. (2024). Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Scientific Reports, 14(1), 18525. https://doi.org/10.1038/s41598-024-69423-2

Merdiansah, R., Siska, S., & Ali Ridha, 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

Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, & Michael Indrawan. (2024). Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z. Journal of Applied Computer Science and Technology, 5(1), 16–25. https://doi.org/10.52158/jacost.v5i1.715

Niharika, and Malhotra, S. (2020). Sentiment Analysis using Artificial Neural Network. International Journal of Recent Technology and Engineering (IJRTE), 8(5), 3267-3273. https://doi.org/10.35940/ijrte.E6450.018520

Nurhasanah, Y. I., & Naufal, M. T. (2024). Klasifikasi Sentimen Pada Buzzer Politik Jelang Pemilu 2024 menggunakan Metode Lexicon-based. MIND Journal, 9(2), 166–178. https://doi.org/10.26760/mindjournal.v9i2.166-178

Nurmadewi, D., Jailani, Z. F., & Manik, N. K. S. (2024). Comparison of the Performance of the VADER and RoBERTa Algorithms on Twitter. SISTEMASI, 13(4), 1547. https://doi.org/10.32520/stmsi.v13i4.4198

O'Toole, K., and Horvat, E.-A. (2024). Extending human creativity with AI. Journal of Creativity, 34(2), 100080. https://doi.org/10.1016/j.yjoc.2024.100080

Pandit, A. R. (2024). Impact of AI in the Animation Industry. International Journal for Research in Applied Science and Engineering Technology, 12(3), 2828–2837. https://doi.org/10.22214/ijraset.2024.59501

Pramudiya, R., Kadafi, A., & Udjulawa, D. (2024). Analisis Sentimen Opini Publik terhadap Kasus Korupsi Timah di Youtube Menggunakan Metode Oversampling dan Algoritma Decision Tree. Arcitech: Journal of Computer Science and Artificial Intelligence, 4(1), 1. https://doi.org/10.29240/arcitech.v4i1.10472

Prastyo, P. H., Sumi, A. S., Dian, A. W., & Permanasari, A. E. (2020). Tweets Responding to the Indonesian Government's Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel. Journal of Information Systems Engineering and Business Intelligence, 6(2), 112. https://doi.org/10.20473/jisebi.6.2.112-122

Putra, Y. P., Okka Adittio Putra, & Willi Novrian. (2025). Systematic Literature Review (SLR) pada Aplikasi Process Mining dalam Transformasi Digital Proses Bisnis. Arcitech: Journal of Computer Science and Artificial Intelligence, 5(1), 90–107. https://doi.org/10.29240/arcitech.v5i1.13888

Rahmadhani, S. A., Rusanti, L. D., & Rosyid, H. Al. (2025). Klasifikasi Sentimen Komentar Youtube Demonstrasi DPR RI Menggunakan Support Vector Machine. Arcitech: Journal of Computer Science and Artificial Intelligence, 5(2), 356–375. https://doi.org/10.29240/arcitech.v5i2.15316

Saadi Rahdi, M., Zamani Boroujeni, F., Abdulhassan, A., Akbari Kopayei, M., & Mohebbi, K. (2025). Enhancing Recommendation Systems with Autoencoder-SVD and Transformer-Based Summarization: A Sentiment-Aware Approach Using GPT-2 and VADER. Qubahan Academic Journal, 5(4), 466–492. https://doi.org/10.48161/qaj.v5n4a2141

Sharma, H., & Juyal, A. (2023). FUTURE OF ANIMATION WITH ARTIFICIAL INTELLIGENCE. ShodhKosh: Journal of Visual and Performing Arts, 4(2SE). https://doi.org/10.29121/shodhkosh.v4.i2SE.2023.559

Tang, M., & Chen, Y. (2024). AI and animated character design: efficiency, creativity, interactivity. The Frontiers of Society, Science and Technology, 6(1). https://doi.org/10.25236/FSST.2024.060120

Troy, & Gunanto, S. G. (2025). The Future of Animation: Exploring the Integration of Generative AI and the Role of Animators. Journal of Information Technology and Its Utilization, 8(1), 29–38. https://doi.org/10.56873/jitu.8.1.6000

Wahyuni, N. A., Ayu, D. P., & Irsyad, H. (2024). Analisis Sentimen di Youtube Terhadap Kenaikan UKT Menggunakan Metode Support Vector Machine. Arcitech: Journal of Computer Science and Artificial Intelligence, 4(1), 57. https://doi.org/10.29240/arcitech.v4i1.10829

Downloads

Published

18-05-2026

How to Cite

Ginanjar Setyo Nugroho, Troy, Samuel Gandang Gunanto, & Venus Khesya Tyasmara. (2026). Analisis Sentimen Publik terhadap Pengaruh Kecerdasan Buatan pada Produksi Animasi Berbasis Data Media Sosial X Menggunakan Metode Lexicon-Based VADER. Arcitech: Journal of Computer Science and Artificial Intelligence, 6(1), 18–41. https://doi.org/10.29240/arcitech.v6i1.16391

Issue

Vol. 6 No. 1 (2026): June 2026

Section

Articles

Citation Check

License

Copyright (c) 2026 Ginanjar Setyo Nugroho, Troy, Samuel Gandang Gunanto, Venus Khesya Tyasmara

Creative Commons License

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

Authors who publish with Arcitech: Journal of Computer science and Artificial Intelligence 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-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment 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 acknowledgment 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).