Public Sentiment Analysis of the Affan Kurniawan Social Issue: A Comparison of Naïve Bayes and SVM Algorithms

Marsella Iriana Mamusung; Lorna Yertas Baisa; Andreas Leonardo Sumendap

doi:10.24014/ijaidm.v9i1.39258

Public Sentiment Analysis of the Affan Kurniawan Social Issue: A Comparison of Naïve Bayes and SVM Algorithms

Marsella Iriana Mamusung, Lorna Yertas Baisa, Andreas Leonardo Sumendap

Abstract

Social media X is a dynamic public space where opinions on social issues, including the Affan Kurniawan case, spread rapidly. This study aims to analyze sentiment distribution, compare the performance of Multinomial Naïve Bayes and Linear Support Vector Machine (LinearSVC), and evaluate classification consistency under a unified evaluation framework. Indonesian-language posts were collected using keyword-based crawling and cleaned from 10,624 to 7,431 valid records (28 August–2 September 2025). The data were preprocessed through normalization, tokenization, stopword removal, and stemming, and labeled into negative, neutral, and positive sentiments using a lexicon-based approach. The results show a dominance of negative sentiment (50.26%), followed by neutral (30.96%) and positive (18.77%). Using Bag-of-Words features and an 80:20 train–test split, LinearSVC outperformed Naïve Bayes with higher accuracy (0.826 vs 0.745) and macro F1-score (0.759 vs 0.579). This study highlights the effectiveness of SVM as a stronger baseline model for Indonesian sentiment classification on social media data.

Keywords

Affan Kurniawan Case; Bag-of-Words; Count Vectorizer; Naïve Bayes; Sentiment Analysis; Support Vector Machine

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References

H. T. Phan, N. T. Nguyen, and D. Hwang, “Sentiment Analysis for Social Media: a Survey,” J. Comput. Sci. Cybern., vol. 37, no. 4, pp. 403–428, 2021, doi: 10.15625/1813-9663/37/4/15892.

Y. Qi and Z. Shabrina, “Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach,” Soc. Netw. Anal. Min., vol. 13, no. 1, pp. 1–14, 2023, doi: 10.1007/s13278-023-01030-x.

S. Lee, Z. Xie, E. Xu, Y. Shao, D. J. Ossip, and D. Li, “Public perceptions of the FDA’s marketing authorization of Vuse on Twitter/X,” Front. Public Heal., vol. 11, no. November 2023, pp. 1–5, 2023, doi: 10.3389/fpubh.2023.1280658.

M. A. Palomino and F. Aider, “Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis,” Appl. Sci. 2022, Vol. 12, Page 8765, vol. 12, no. 17, p. 8765, Aug. 2022, doi: 10.3390/APP12178765.

Q. A. Xu, V. Chang, and C. Jayne, “A systematic review of social media-based sentiment analysis: Emerging trends and challenges,” Decis. Anal. J., vol. 3, p. 100073, Jun. 2022, doi: 10.1016/J.DAJOUR.2022.100073.

L. H. X. Ng and K. M. Carley, “A global comparison of social media bot and human characteristics,” Sci. Rep., vol. 15, no. 1, pp. 1–18, 2025, doi: 10.1038/s41598-025-96372-1.

H. Wan, M. Luo, Z. Ma, G. Dai, and X. Zhao, “How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis,” pp. 31481–31504, 2025, doi: 10.18653/v1/2025.emnlp-main.1604.

A. Upadhyaya, M. Fisichella, and W. Nejdl, “Towards sentiment and Temporal Aided Stance Detection of climate change tweets,” Inf. Process. Manag., vol. 60, no. 4, p. 103325, Jul. 2023, doi: 10.1016/J.IPM.2023.103325.

A. R. Sembiring and C. K. Dewa, “Sentiment Analysis On Indonesian Tweets about the 2024 Election,” Sinkron, vol. 9, no. 1, pp. 413–422, 2025, doi: 10.33395/sinkron.v9i1.14481.

Y. Wang, J. Guo, C. Yuan, and B. Li, “Sentiment Analysis of Twitter Data,” Appl. Sci., vol. 12, no. 22, pp. 1–14, 2022, doi: 10.3390/app122211775.

H. T. Wijaya and K. Kustiyono, “Sentiment Analysis of Twitter Users Towards the Kartu Prakerja Program Using the Naive Bayes Method,” Int. J. Adv. Data Inf. Syst., vol. 5, no. 2, pp. 242–252, 2024, doi: 10.59395/ijadis.v5i2.1342.

S. Barreto, R. Moura, J. Carvalho, A. Paes, and A. Plastino, “Sentiment analysis in tweets: an assessment study from classical to modern word representation models,” Data Min. Knowl. Discov. 2022 371, vol. 37, no. 1, pp. 318–380, Nov. 2022, doi: 10.1007/S10618-022-00853-0.

A. S. Muliana, D. Lestarini, and S. P. Raflesia, “Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X,” Sistemasi, vol. 13, no. 6, p. 2467, 2024, doi: 10.32520/stmsi.v13i6.4592.

Riyantoro, “Sentiment Analysis of Twitter Data on Indonesia’s Cabinet Using Naïve Bayes and Support Vector Machine Algorithms,” vol. 10, no. April, p. 2024, 2024.

J. Y. M. Nip and B. Berthelier, “Social Media Sentiment Analysis,” Encyclopedia, vol. 4, no. 4, pp. 1590–1598, 2024, doi: 10.3390/encyclopedia4040104.

N. Nyoman, A. Sri, I. G. I. Sudipa, N. Nyoman, A. J. Sastaparamitha, and A. Gede, “Public Response on X to the Revocation of Indonesia ’ s 3-Kg LPG Retail Ban : A Support Vector Machine Study,” vol. 6, no. 2, pp. 412–425.

R. Murtfeldt, N. Alterman, I. Kahveci, and J. D. West, “RIP Twitter API: A eulogy to its vast research contributions,” pp. 1–34, 2024, [Online]. Available: http://arxiv.org/abs/2404.07340

P. Paper, H. D. Sharma, and P. Goyal, “An Analysis of Sentiment : Methods , Applications ,” no. Ml, 2023.

J. O. Leandro and M. I. Fianty, “Evaluation of Sentiment Analysis Methods for Social Media Applications: A Comparison of Support Vector Machines and Naïve Bayes,” Int. J. Informatics Vis., vol. 9, no. 2, pp. 796–807, 2025, doi: 10.62527/joiv.9.2.2905.

M. Graff, D. Moctezuma, and E. S. Téllez, “Bag-of-Word approach is not dead: A performance analysis on a myriad of text classification challenges,” Nat. Lang. Process. J., vol. 11, p. 100154, Jun. 2025, doi: 10.1016/J.NLP.2025.100154.

T. R. Widodo, I. N. Fajri, and B. W. Sari, “Sentiment Analysis of the Film ‘JUMBO’ on Twitter Using the Naive Bayes Method and Support Vector Machine (SVM) with a Text Mining Approach,” J. Appl. Informatics Comput., vol. 9, no. 5, pp. 2861–2868, 2025, doi: 10.30871/jaic.v9i5.10557.

N. Wayan, I. Juliandewi, A. S. Kusuma, K. Martina, D. Putri, and I. Gusti, “Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube,” vol. 6, no. 3, pp. 527–537, 2024.

N. A. Maulana and D. Darwis, “Perbandingan Metode SVM dan Naïve Bayes untuk Analisis Sentimen pada Twitter tentang Obesitas di Kalangan Gen Z,” J. Pendidik. dan Teknol. Indones., vol. 5, no. 3, pp. 655–666, 2025, doi: 10.52436/1.jpti.691.

O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-56706-x.

Y. Mao, Q. Liu, and Y. Zhang, “Sentiment analysis methods, applications, and challenges: A systematic literature review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 4, p. 102048, 2024, doi: 10.1016/j.jksuci.2024.102048.

DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.39258

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