Quick jump to page content
  • Main Navigation
  • Main Content
  • Sidebar

  • Home
  • Current
  • Archives
  • Join As Reviewer
  • Info
  • Announcements
  • Statistics
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  • Register
  • Login
  • Home
  • Current
  • Archives
  • Join As Reviewer
  • Info
  • Announcements
  • Statistics
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  1. Home
  2. Archives
  3. Vol. 10, No. 3, August 2025
  4. Articles

Issue

Vol. 10, No. 3, August 2025

Issue Published : Jun 13, 2025
Creative Commons License

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

Analysis of Public Opinion on The Governor Candidate Debate Using LDA and IndoBERT

https://doi.org/10.22219/kinetik.v10i3.2221
Ahmad Abdul Chamid
UMK
Ratih Nindyasari
Universitas Muria Kudus
Noor Azizah
Universitas Islam Nahdlatul Ulama Jepara
Ahmad Hariyadi
Universitas Muria Kudus

Corresponding Author(s) : Ahmad Abdul Chamid

abdul.chamid@umk.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 3, August 2025
Article Published : Jun 13, 2025

Share
WA Share on Facebook Share on Twitter Pinterest Email Telegram
  • Abstract
  • Cite
  • References
  • Authors Details

Abstract

The gubernatorial candidate debate was broadcast live streaming through various YouTube channels, which attracted public attention. Many discussions and conversations appeared in the comments section of each YouTube channel that broadcasted the debate. Given the numerous public discussions, it is undoubtedly interesting to analyze the contents of the conversations, as well as the expectations and feedback from the public. However, analyzing conversations in the form of text data will be challenging using conventional methods. Therefore, in this study, public opinion will be analyzed using the topic identification and sentiment classification approaches. Topic identification is conducted to obtain accurate information about what the public is discussing, while sentiment classification is used to determine whether each comment contains positive or negative sentiments. This research is novel because it utilizes data collected from various major media YouTube channels and includes a qualitative analysis of the findings. This study uses public comment data taken from the KPU, NarasiTV, and KompasTV YouTube channels; the results obtained included 4,147 data points. Data preprocessing involves identifying topics using the LDA method, evaluating the LDA model, performing sentiment classification using IndoBERT, and visualizing the results of the public opinion analysis. The results revealed five topics with a perplexity value of -7.7909 and a coherence score of 0.5109. In addition, topic 4 is the most dominant compared to other topics, with 1,146 comments classified as positive sentiment and 504 classified as negative sentiment. Topic 4 reflects how religion, culture, and frequently mentioned figures are perceived and discussed by the public, especially in relation to the gubernatorial election (pilgub) or gubernatorial candidate debates.

Keywords

Analysis of Public Opinion Topic Modeling Sentiment Classification LDA BERT
Chamid, A. A., Nindyasari, R., Azizah, N., & Hariyadi, A. (2025). Analysis of Public Opinion on The Governor Candidate Debate Using LDA and IndoBERT. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(3), 295-306. https://doi.org/10.22219/kinetik.v10i3.2221
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Komisi Pemilihan Umum, Peraturan Komisi Pemilihan Umum. Indonesia, 2024.
  2. I. Gjorshoska, A. Dedinec, J. Prodanova, A. Dedinec, and L. Kocarev, “Public perception of waste regulations implementation. Natural language processing vs real GHG emission reduction modeling,” Ecol Inform, vol. 76, Sep. 2023. https://doi.org/10.1016/j.ecoinf.2023.102130
  3. O. Olabanjo et al., “From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election,” Heliyon, vol. 9, no. 5, May 2023. https://doi.org/10.1016/j.heliyon.2023.e16085
  4. S. Ha and E. Grubert, “Hybridizing qualitative coding with natural language processing and deep learning to assess public comments: A case study of the clean power plan,” Energy Res Soc Sci, vol. 98, Apr. 2023. https://doi.org/10.1016/j.erss.2023.103016
  5. A. A. Chamid, Widowati, and R. Kusumaningrum, “Multi-Label Text Classification on Indonesian User Reviews Using Semi-Supervised Graph Neural Networks,” ICIC Express Letters, vol. 17, no. 10, pp. 1075–1084, 2023. https://doi.org/10.24507/icicel.17.10.1075
  6. A. A. Chamid, Widowati, and R. Kusumaningrum, “Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis,” Big Data and Cognitive Computing, vol. 7, no. 1, p. 5, 2023. https://doi.org/10.3390/bdcc7010005
  7. M. C. Rahmadan, A. N. Hidayanto, D. S. Ekasari, B. Purwandari, and Theresiawati, “Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter,” in International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) Sentiment, 2020, pp. 126–130. https://doi.org/10.1109/ICIMCIS51567.2020.9354320
  8. M. Paramarta and J. B. B. Darmawan, “Implementasi Metode Support Vector Machine dalam Analisis Sentimen Opini Masyarakat Terhadap Pilkada 2020 pada Media Sosial Twitter,” in Prosiding Nasional Rekayasa Teknologi Industri dan Informasi XVIII, Nov. 2023, pp. 836–841.
  9. A. Rahmawati, A. Marjuni, and J. Zeniarja, “Analisis Sentimen Publik Pada Media Sosial Twitter Terhadap Pelaksanaan Pilkada Serentak Menggunakan Algoritma Support Vector Machine,” CCIT Journal, vol. 10, no. 2, pp. 197–206, 2017. https://doi.org/10.33050/ccit.v10i2.539
  10. R. Pohan et al., “Implementasi Algoritma Support Vector Machine dan Model Bag-of-Words dalam Analisis Sentimen mengenai PILKADA 2020 pada Pengguna Twitter,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 10, pp. 4924–4931, 2022.
  11. A. Muzaki and A. Witanti, “Sentimen Analisis Masyarakat Di Twitter Terhadap Pilkada 2020 Ditengah Pandemic Covid-19 Dengan Metode NaïVe Bayes Classifier,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 101–107, 2021. https://doi.org/10.20884/1.jutif.2021.2.2.51
  12. S. N. Listyarini and D. A. Anggoro, “Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 261–268, 2021. https://doi.org/10.52436/1.jpti.60
  13. N. Habbat, H. Anoun, and L. Hassouni, “Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic,” Indonesian Journal of Innovation and Applied Sciences (IJIAS), vol. 2, no. 1, pp. 60–67, 2022. https://doi.org/10.47540/ijias.v2i1.432
  14. I. Alagha, “Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives,” Journal of Information Science Theory and Practice, vol. 9, no. 1, pp. 35–53, 2021. https://doi.org/10.1633/JISTaP.2021.9.1.3
  15. A. Verbytska, “Topic modelling as a method for framing analysis of news coverage of the Russia-Ukraine war in 2022–2023,” Lang Commun, vol. 99, pp. 174–193, Nov. 2024. https://doi.org/10.1016/j.langcom.2024.10.004
  16. S. Ying, “Guests’ Aesthetic experience with lifestyle hotels: An application of LDA topic modelling analysis,” Heliyon, vol. 10, no. 16, Aug. 2024. https://doi.org/10.1016/j.heliyon.2024.e35894
  17. S. E. Uthirapathy and D. Sandanam, “Topic Modelling and Opinion Analysis on Climate Change Twitter Data Using LDA and BERT Model.,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 908–917. https://doi.org/10.1016/j.procs.2023.01.071
  18. M. N. P. Ma’ady, A. F. A. Rahim, T. S. N. Syahda, A. F. Rizqi, and M. C. A. Ratna, “Malaysia Citizen Sentiment on Government Response Towards Covid-19 Disaster Management: Using LDA-based Topic Visualization on Twitter,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 561–569. https://doi.org/10.1016/j.procs.2024.03.040
  19. K. Taha, P. D. Yoo, C. Yeun, D. Homouz, and A. Taha, “A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights,” Nov. 01, 2024, Elsevier Ireland Ltd. https://doi.org/10.1016/j.cosrev.2024.100664
  20. A. A. Chamid, Widowati, and R. Kusumaningrum, “Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method,” Ingénierie des Systèmes d’Information, vol. 29, no. 1, pp. 161–167, 2024. https://doi.org/10.18280/isi.290118
  21. Supriyono, A. P. Wibawa, Suyono, and F. Kurniawan, “Advancements in natural language processing: Implications, challenges, and future directions,” Telematics and Informatics Reports, vol. 16, Dec. 2024. https://doi.org/10.1016/j.teler.2024.100173
  22. A. A. Firdaus, A. Yudhana, and I. Riadi, “Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2023. https://doi.org/10.22219/kinetik.v8i2.1686
  23. A. A. Chamid, W. Widowati, and R. Kusumaningrum, “Text data labeling process for semi-supervised learning modeling,” 12TH INTERNATIONAL SEMINAR ON NEW PARADIGM AND INNOVATION ON NATURAL SCIENCES AND ITS APPLICATIONS (12TH ISNPINSA): Contribution of Science and Technology in the Changing World, vol. 3165, p. 030011, 2024. https://doi.org/10.1063/5.0216320
  24. A. W. Pradana and M. Hayaty, “The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 375–380, Oct. 2019. https://doi.org/10.22219/kinetik.v4i4.912
  25. J. Zimmermann, L. E. Champagne, J. M. Dickens, and B. T. Hazen, “Approaches to improve preprocessing for Latent Dirichlet Allocation topic modeling,” Decis Support Syst, vol. 185, Oct. 2024. https://doi.org/10.1016/j.dss.2024.114310
  26. Y. Jiang, M. Fu, J. Fang, M. Rossi, Y. Wang, and C. W. Tan, “Advancing an LDA-GMM-CorEx topic model with prior domain knowledge in information systems research,” Information and Management, vol. 62, no. 2, Mar. 2025. https://doi.org/10.1016/j.im.2024.104097
  27. D. Colla, M. Delsanto, M. Agosto, B. Vitiello, and D. P. Radicioni, “Semantic coherence markers: The contribution of perplexity metrics,” Artif Intell Med, vol. 134, Dec. 2022. https://doi.org/10.1016/j.artmed.2022.102393
  28. R. He, C. Palominos, H. Zhang, M. F. Alonso-Sánchez, L. Palaniyappan, and W. Hinzen, “Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models,” Psychiatry Res, vol. 333, Mar. 2024. https://doi.org/10.1016/j.psychres.2024.115752
  29. T. Cohen, W. Xu, Y. Guo, S. Pakhomov, and G. Leroy, “Coherence and comprehensibility: Large language models predict lay understanding of health-related content,” J Biomed Inform, vol. 161, Jan. 2025. https://doi.org/10.1016/j.jbi.2024.104758
  30. Q. Xie, X. Zhang, Y. Ding, and M. Song, “Monolingual and multilingual topic analysis using LDA and BERT embeddings,” J Informetr, vol. 14, no. 3, Aug. 2020. https://doi.org/10.1016/j.joi.2020.101055
  31. J. Liu, R. Long, H. Chen, M. Wu, W. Ma, and Q. Li, “Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model,” J Environ Manage, vol. 371, Dec. 2024, doi: 10.1016/j.jenvman.2024.123112.
  32. J. Lim and J. Hwang, “Exploring diverse interests of collaborators in smart cities: A topic analysis using LDA and BERT,” Heliyon, vol. 10, no. 9, May 2024, doi: 10.1016/j.heliyon.2024.e30367.
  33. H. J. Juandri, H. Hasmawati, and B. Bunyamin, “Aspect-Level Sentiment Analysis on GoPay App Reviews Using Multilayer Perceptron and Word Embeddings,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Aug. 2024. https://doi.org/10.22219/kinetik.v9i4.2041
  34. R. A. Rajagede, “Improving Automatic Essay Scoring for Indonesian Language using Simpler Model and Richer Feature,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 11–18, Feb. 2021. https://doi.org/10.22219/kinetik.v6i1.1196
  35. A. Salsabil, E. B. Setiawan, and I. Kurniawan, “Content-based filtering movie recommender system using semantic approach with recurrent neural network classification and SGD,” Computer Network, Computing, Electronics, and Control Journal, vol. 9, no. 2, pp. 193–202, 2024. https://doi.org/10.22219/kinetik.v9i2.1940
  36. A. B. Y. A. Putra, Y. Sibaroni, and A. F. Ihsan, “Disinformation Detection on 2024 Indonesia Presidential Election using IndoBERT,” in 2023 International Conference on Data Science and Its Applications, ICoDSA 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 350–355. https://doi.org/10.1109/ICoDSA58501.2023.10277572
  37. R. I. Yulfa, B. H. Setiawan, G. G. Lourensius, and K. Purwandari, “Enhancing Hate Speech Detection in Social Media Using IndoBERT Model: A Study of Sentiment Analysis during the 2024 Indonesia Presidential Election,” in ICCA 2023 - 2023 5th International Conference on Computer and Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023. https://doi.org/10.1109/ICCA59364.2023.10401700
Read More

References


Komisi Pemilihan Umum, Peraturan Komisi Pemilihan Umum. Indonesia, 2024.

I. Gjorshoska, A. Dedinec, J. Prodanova, A. Dedinec, and L. Kocarev, “Public perception of waste regulations implementation. Natural language processing vs real GHG emission reduction modeling,” Ecol Inform, vol. 76, Sep. 2023. https://doi.org/10.1016/j.ecoinf.2023.102130

O. Olabanjo et al., “From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election,” Heliyon, vol. 9, no. 5, May 2023. https://doi.org/10.1016/j.heliyon.2023.e16085

S. Ha and E. Grubert, “Hybridizing qualitative coding with natural language processing and deep learning to assess public comments: A case study of the clean power plan,” Energy Res Soc Sci, vol. 98, Apr. 2023. https://doi.org/10.1016/j.erss.2023.103016

A. A. Chamid, Widowati, and R. Kusumaningrum, “Multi-Label Text Classification on Indonesian User Reviews Using Semi-Supervised Graph Neural Networks,” ICIC Express Letters, vol. 17, no. 10, pp. 1075–1084, 2023. https://doi.org/10.24507/icicel.17.10.1075

A. A. Chamid, Widowati, and R. Kusumaningrum, “Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis,” Big Data and Cognitive Computing, vol. 7, no. 1, p. 5, 2023. https://doi.org/10.3390/bdcc7010005

M. C. Rahmadan, A. N. Hidayanto, D. S. Ekasari, B. Purwandari, and Theresiawati, “Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter,” in International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) Sentiment, 2020, pp. 126–130. https://doi.org/10.1109/ICIMCIS51567.2020.9354320

M. Paramarta and J. B. B. Darmawan, “Implementasi Metode Support Vector Machine dalam Analisis Sentimen Opini Masyarakat Terhadap Pilkada 2020 pada Media Sosial Twitter,” in Prosiding Nasional Rekayasa Teknologi Industri dan Informasi XVIII, Nov. 2023, pp. 836–841.

A. Rahmawati, A. Marjuni, and J. Zeniarja, “Analisis Sentimen Publik Pada Media Sosial Twitter Terhadap Pelaksanaan Pilkada Serentak Menggunakan Algoritma Support Vector Machine,” CCIT Journal, vol. 10, no. 2, pp. 197–206, 2017. https://doi.org/10.33050/ccit.v10i2.539

R. Pohan et al., “Implementasi Algoritma Support Vector Machine dan Model Bag-of-Words dalam Analisis Sentimen mengenai PILKADA 2020 pada Pengguna Twitter,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 10, pp. 4924–4931, 2022.

A. Muzaki and A. Witanti, “Sentimen Analisis Masyarakat Di Twitter Terhadap Pilkada 2020 Ditengah Pandemic Covid-19 Dengan Metode NaïVe Bayes Classifier,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 101–107, 2021. https://doi.org/10.20884/1.jutif.2021.2.2.51

S. N. Listyarini and D. A. Anggoro, “Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 261–268, 2021. https://doi.org/10.52436/1.jpti.60

N. Habbat, H. Anoun, and L. Hassouni, “Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic,” Indonesian Journal of Innovation and Applied Sciences (IJIAS), vol. 2, no. 1, pp. 60–67, 2022. https://doi.org/10.47540/ijias.v2i1.432

I. Alagha, “Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives,” Journal of Information Science Theory and Practice, vol. 9, no. 1, pp. 35–53, 2021. https://doi.org/10.1633/JISTaP.2021.9.1.3

A. Verbytska, “Topic modelling as a method for framing analysis of news coverage of the Russia-Ukraine war in 2022–2023,” Lang Commun, vol. 99, pp. 174–193, Nov. 2024. https://doi.org/10.1016/j.langcom.2024.10.004

S. Ying, “Guests’ Aesthetic experience with lifestyle hotels: An application of LDA topic modelling analysis,” Heliyon, vol. 10, no. 16, Aug. 2024. https://doi.org/10.1016/j.heliyon.2024.e35894

S. E. Uthirapathy and D. Sandanam, “Topic Modelling and Opinion Analysis on Climate Change Twitter Data Using LDA and BERT Model.,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 908–917. https://doi.org/10.1016/j.procs.2023.01.071

M. N. P. Ma’ady, A. F. A. Rahim, T. S. N. Syahda, A. F. Rizqi, and M. C. A. Ratna, “Malaysia Citizen Sentiment on Government Response Towards Covid-19 Disaster Management: Using LDA-based Topic Visualization on Twitter,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 561–569. https://doi.org/10.1016/j.procs.2024.03.040

K. Taha, P. D. Yoo, C. Yeun, D. Homouz, and A. Taha, “A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights,” Nov. 01, 2024, Elsevier Ireland Ltd. https://doi.org/10.1016/j.cosrev.2024.100664

A. A. Chamid, Widowati, and R. Kusumaningrum, “Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method,” Ingénierie des Systèmes d’Information, vol. 29, no. 1, pp. 161–167, 2024. https://doi.org/10.18280/isi.290118

Supriyono, A. P. Wibawa, Suyono, and F. Kurniawan, “Advancements in natural language processing: Implications, challenges, and future directions,” Telematics and Informatics Reports, vol. 16, Dec. 2024. https://doi.org/10.1016/j.teler.2024.100173

A. A. Firdaus, A. Yudhana, and I. Riadi, “Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2023. https://doi.org/10.22219/kinetik.v8i2.1686

A. A. Chamid, W. Widowati, and R. Kusumaningrum, “Text data labeling process for semi-supervised learning modeling,” 12TH INTERNATIONAL SEMINAR ON NEW PARADIGM AND INNOVATION ON NATURAL SCIENCES AND ITS APPLICATIONS (12TH ISNPINSA): Contribution of Science and Technology in the Changing World, vol. 3165, p. 030011, 2024. https://doi.org/10.1063/5.0216320

A. W. Pradana and M. Hayaty, “The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 375–380, Oct. 2019. https://doi.org/10.22219/kinetik.v4i4.912

J. Zimmermann, L. E. Champagne, J. M. Dickens, and B. T. Hazen, “Approaches to improve preprocessing for Latent Dirichlet Allocation topic modeling,” Decis Support Syst, vol. 185, Oct. 2024. https://doi.org/10.1016/j.dss.2024.114310

Y. Jiang, M. Fu, J. Fang, M. Rossi, Y. Wang, and C. W. Tan, “Advancing an LDA-GMM-CorEx topic model with prior domain knowledge in information systems research,” Information and Management, vol. 62, no. 2, Mar. 2025. https://doi.org/10.1016/j.im.2024.104097

D. Colla, M. Delsanto, M. Agosto, B. Vitiello, and D. P. Radicioni, “Semantic coherence markers: The contribution of perplexity metrics,” Artif Intell Med, vol. 134, Dec. 2022. https://doi.org/10.1016/j.artmed.2022.102393

R. He, C. Palominos, H. Zhang, M. F. Alonso-Sánchez, L. Palaniyappan, and W. Hinzen, “Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models,” Psychiatry Res, vol. 333, Mar. 2024. https://doi.org/10.1016/j.psychres.2024.115752

T. Cohen, W. Xu, Y. Guo, S. Pakhomov, and G. Leroy, “Coherence and comprehensibility: Large language models predict lay understanding of health-related content,” J Biomed Inform, vol. 161, Jan. 2025. https://doi.org/10.1016/j.jbi.2024.104758

Q. Xie, X. Zhang, Y. Ding, and M. Song, “Monolingual and multilingual topic analysis using LDA and BERT embeddings,” J Informetr, vol. 14, no. 3, Aug. 2020. https://doi.org/10.1016/j.joi.2020.101055

J. Liu, R. Long, H. Chen, M. Wu, W. Ma, and Q. Li, “Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model,” J Environ Manage, vol. 371, Dec. 2024, doi: 10.1016/j.jenvman.2024.123112.

J. Lim and J. Hwang, “Exploring diverse interests of collaborators in smart cities: A topic analysis using LDA and BERT,” Heliyon, vol. 10, no. 9, May 2024, doi: 10.1016/j.heliyon.2024.e30367.

H. J. Juandri, H. Hasmawati, and B. Bunyamin, “Aspect-Level Sentiment Analysis on GoPay App Reviews Using Multilayer Perceptron and Word Embeddings,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Aug. 2024. https://doi.org/10.22219/kinetik.v9i4.2041

R. A. Rajagede, “Improving Automatic Essay Scoring for Indonesian Language using Simpler Model and Richer Feature,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 11–18, Feb. 2021. https://doi.org/10.22219/kinetik.v6i1.1196

A. Salsabil, E. B. Setiawan, and I. Kurniawan, “Content-based filtering movie recommender system using semantic approach with recurrent neural network classification and SGD,” Computer Network, Computing, Electronics, and Control Journal, vol. 9, no. 2, pp. 193–202, 2024. https://doi.org/10.22219/kinetik.v9i2.1940

A. B. Y. A. Putra, Y. Sibaroni, and A. F. Ihsan, “Disinformation Detection on 2024 Indonesia Presidential Election using IndoBERT,” in 2023 International Conference on Data Science and Its Applications, ICoDSA 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 350–355. https://doi.org/10.1109/ICoDSA58501.2023.10277572

R. I. Yulfa, B. H. Setiawan, G. G. Lourensius, and K. Purwandari, “Enhancing Hate Speech Detection in Social Media Using IndoBERT Model: A Study of Sentiment Analysis during the 2024 Indonesia Presidential Election,” in ICCA 2023 - 2023 5th International Conference on Computer and Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023. https://doi.org/10.1109/ICCA59364.2023.10401700

Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 0 Download : 0

Downloads

Download data is not yet available.

Quick Link

  • Author Guidelines
  • Download Manuscript Template
  • Peer Review Process
  • Editorial Board
  • Reviewer Acknowledgement
  • Aim and Scope
  • Publication Ethics
  • Licensing Term
  • Copyright Notice
  • Open Access Policy
  • Important Dates
  • Author Fees
  • Indexing and Abstracting
  • Archiving Policy
  • Scopus Citation Analysis
  • Statistic
  • Article Withdrawal

Meet Our Editorial Team

Ir. Amrul Faruq, M.Eng., Ph.D
Editor in Chief
Universitas Muhammadiyah Malang
Google Scholar Scopus
Agus Eko Minarno
Editorial Board
Universitas Muhammadiyah Malang
Google Scholar  Scopus
Hanung Adi Nugroho
Editorial Board
Universitas Gadjah Mada
Google Scholar Scopus
Roman Voliansky
Editorial Board
Dniprovsky State Technical University, Ukraine
Google Scholar Scopus
Read More
 

KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
eISSN : 2503-2267
pISSN : 2503-2259


Address

Program Studi Elektro dan Informatika

Fakultas Teknik, Universitas Muhammadiyah Malang

Jl. Raya Tlogomas 246 Malang

Phone 0341-464318 EXT 247

Contact Info

Principal Contact

Amrul Faruq
Phone: +62 812-9398-6539
Email: faruq@umm.ac.id

Support Contact

Fauzi Dwi Setiawan Sumadi
Phone: +62 815-1145-6946
Email: fauzisumadi@umm.ac.id

© 2020 KINETIK, All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License