SENTIMENT ANALYSIS OF GOVERNMENT ON TIKTOK AND X PLATFORMS WITH SVM AND SMOTE APPROACH
DOI:
https://doi.org/10.33480/jitk.v10i4.6645Keywords:
sentiment analysis, SMOTE, support vector machine, TikTok, XAbstract
This study aims to analyze public sentiment toward the government on TikTok and X (formerly Twitter) using the Support Vector Machine (SVM) algorithm optimized with the Synthetic Minority Over-sampling Technique (SMOTE). Data were collected through keyword-based scraping of posts containing the word “pemerintah” (government) and processed using standard NLP pre-processing techniques. Results show that SVM combined with SMOTE significantly improves classification accuracy from 61% to 76% on TikTok, and from 74% to 86% on X. Word cloud analysis confirms these findings: TikTok content tends to reflect neutral and positive sentiments, while X contains predominantly negative expressions. These differences highlight platform-specific public discourse characteristics. The findings suggest that public communication strategies should be tailored accordingly: TikTok for positive narrative and outreach, X for monitoring feedback and criticism. This approach demonstrates the effectiveness of machine learning-based sentiment analysis in supporting data-driven public policy communication.
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T. Dewi Salma, M. Ferdi Kurniawan, R. Darmawan, and A. Basri, “Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama,” vol 9, no 2, pp. 757-764, Jan. 2025, doi: https://doi.org/10.35870/jtik.v9i2.3535
Y. Rum Zattayu Mustopo, “Analisis Sentimen Proyek Strategis Nasional Food Estate Menggunakan Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine,” vol 9 no 2, pp. 485-494, 2025, doi: https://doi.org/10.35870/jtik.v9i2.3312
We Are Social Indonesia, “DIGITAL 2023 INDONESIA,” 2023. Accessed: Mar. 24, 2025. [Online]. Available: https://wearesocial.com/id/blog/2023/01/digital-2023/
S. Widiasih, F. Julina, and I. Septiani Salsabila, “Analisis Sosial Media Pemerintah Daerah Di Indonesia Berdasarkan Respons Warganet,” Jurnal Ilmiah Riset dan Pengembangan |, vol. 9, Dec. 2024.
A. Sadat, H. Lawelai, and ) Ansar Suherman, “Analisis Sentimen Media Sosial: Hate Speech Kepada Pemerintah Di Twitter,” PRAJA: Jurnal Ilmiah Pemerintahan, vol. 10, p. 2022, Feb. 2022.
F. Rachmawati, U. Azmi, and R. Azwarini, “Comparison of Lexicon-based Methods and Bidirectional Encoder Representations for Transformers Models in Sentiment Analysis of Government Debt Market Movements,” International Journal of Engi-neering and Computer Science Applications (IJECSA), vol. 4, no. 1, pp. 13–28, 2025, doi: 10.30812/ijecsa.v4i1.4832.
A. A. Al Kaafi, S. Suparni, H. Rachmi, A. Maulana, and R. Nurtriani, “Optimizing Twitter Sentiment Analysis on Tapera Policy Using SVM and PSO,” sinkron, vol. 9, no. 1, pp. 167–176, Jan. 2025, doi: 10.33395/sinkron.v9i1.14227.
Y. A. Singgalen, S. Y. Wahyuningtyas, Y. E. Widodo, M. N. A. Dasra, and R. W. Setiawan, “Travel Vlog Reviews: Support Vector Machine Performance in Sentiment Classification,” Ingenierie des Systemes d’Information, vol. 30, no. 1, pp. 101–110, Jan. 2025, doi: 10.18280/isi.300109.
P. P. Putra, M. K. Anam, A. S. Chan, A. Hadi, N. Hendri, and A. Masnur, “Optimizing Sentiment Analysis on Imbalanced Hotel Review Data Using SMOTE and Ensemble Machine Learning Techniques,” Journal of Applied Data Sciences, vol. 6, no. 2, pp. 936–951, May 2025, doi: 10.47738/jads.v6i2.618.
Dewi ArdikaTeresia and Mailoa Evangs, “Perbandingan Implementasi Metode SMOTE pada Algoritma Support Vector Machine (SVM) dalam Analisis Sentimen Opini Masyarakat Tentang Mixue Artikel Ilmiah,” Jakarta, Apr. 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,” Tanggerang, Apr. 2025. doi: http://dx.doi.org/10.62527/joiv.9.2.2905.
Faisal Muhammad et al., “A Hybrid MOO, MCGDM, and Sentiment Analysis Methodologies for Enhancing Regional Expansion Planning: A Case Study Luwu - Indonesia.,” International Journal of Mathematical Engineering and Management Sciences, vol. 10, pp. 163–188, Jan. 2025, doi: 10.33889/IJMEMS.2025.10.1.010.
R. A. Saemani and N. Setiyawati, “Analisis Sentimen Permendikbud Pencegahan Dan Penanganan Kekerasan Seksual Di Lingkungan Perguruan Tinggi Menggunakan Support Vector Machine (SVM),” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 8, no. 1, pp. 65–71, Aug. 2022, doi: 10.33480/jitk.v8i1.2807.
M. I. Prayugah and N. Ariyanti, “Analisis Sentimen Publik Pada Pemerintah Dalam Serangan Ransomware Dengan Pendekatan Smote,” Journal Of Information Systems And Informatics Engineering, vol. 8, no. 2, pp. 333–343, Nov. 2024, doi: 10.35145/joisie.v8i2.4764.
Y. O. Sihombing, “Optimasi Model IndoRoBERTa-Base Untuk Klasifikasi Sentimen dan Emosi Pada Komentar Publik Twitter BKN,” Institut Teknologi Sepuluh Nopember, 2025.
A. Muzaki and A. Witanti, “Sentiment Analysis Of The Community In The Twitter To The 2020 Election In Pandemic Covid-19 By Method Naive Bayes Classifier,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 101–107, Mar. 2021, doi: 10.20884/1.jutif.2021.2.2.51.
Alisya Mutia Mantika, Agung Triayudi, and Rima Tamara Aldisa, “Sentiment Analysis on Twitter Using Naïve Bayes and Logistic Regression for the 2024 Presidential Election,” SaNa: Journal of Blockchain, NFTs and Metaverse Technology, vol. 2, no. 1, pp. 44–55, Feb. 2024, doi: 10.58905/sana.v2i1.267.
W. Maharani, H. Daud, N. Muhammad, and E. A. Kadir, “Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method,” Journal of Information Systems Engineering and Business Intelligence, vol. 10, no. 3, pp. 392–407, Oct. 2024, doi: 10.20473/jisebi.10.3.392-407.
D. P. Alamsyah, T. Arifin, Y. Ramdhani, F. A. Hidayat, and L. Susanti, “Classification of Customer Complaints: TF-IDF Approaches,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), 2022, pp. 1–5. doi: 10.1109/CONIT55038.2022.9848056.
A. Gandhi et al., “Hate speech detection: A comprehensive review of recent works,” Expert Syst, vol. 41, no. 8, p. e13562, 2024, doi: https://doi.org/10.1111/exsy.13562.
Y. A. Singgalen, “Comparative Analysis of DT and SVM Model Performance with SMOTE in Sentiment Classification,” 2024, doi: 10.30865/klik.v4i5.1828.
V. H. Pranatawijaya, N. N. K. Sari, R. A. Rahman, E. Christian, and S. Geges, “Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews,” Journal of Information Systems Engineering and Business Intelligence, vol. 10, no. 3, pp. 328–339, Oct. 2024, doi: 10.20473/jisebi.10.3.328-339.
M. T. Uliniansyah et al., “Twitter dataset on public sentiments towards biodiversity policy in Indonesia,” Data Brief, vol. 52, Feb. 2024, doi: 10.1016/j.dib.2023.109890.
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Copyright (c) 2025 Dimar Pateman, Tri Ferga Prasetyo, Harun Sujadi
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