Analisis Sentimen Publik Pada Platform X Menggunakan Algoritma Naïve Bayes: Isu Ijazah Presiden Jokowi
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Abstract
Perkembangan media sosial telah membuka ruang luas bagi masyarakat untuk mengekspresikan pendapat terhadap berbagai isu politik di Indonesia. Salah satu topik yang menimbulkan perhatian publik adalah dugaan ijazah palsu Presiden Joko Widodo (Jokowi) yang menimbulkan beragam tanggapan di aplikasi X. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap isu keaslian ijazah tersebut menggunakan algoritma Naive Bayes Classifier. Sebanyak 1.004 tweet berbahasa Indonesia dikumpulkan melalui proses scraping dan dibagi menjadi 80% data latih serta 20% data uji. Tahapan analisis meliputi preprocessing data dan klasifikasi menggunakan algoritma Naive Bayes. Hasil pengujian menunjukkan akurasi sebesar 76,6%, precision 56,8%, recall 40,4%, dan F1-score 47,2%. Berdasarkan confusion matrix, model lebih baik dalam mengidentifikasi sentimen negatif, sehingga algoritma ini cukup efektif untuk analisis sentimen politik pada platform X.
References
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[2] D. Sugiarto, E. Utami, and A. Yaqin, “Perbandingan Kinerja Model TF-IDF dan BOW untuk Klasifikasi Opini Publik Tentang Kebijakan BLT Minyak Goreng,” 2022.
[3] T. Santoso and A. A. Yana, “Sentiment analysis of Facebook comments on Indonesian presidential candidates using the Naïve Bayes method,” in Journal of Physics: Conference Series, 2020, p. 012012. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1641/1/012012
[4] A. Mustolih, P. Arsi, and P. Subarkah, “Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 6, no. 2, p. 231, Aug. 2023, doi: 10.24014/ijaidm.v6i2.24864.
[5] C. Setianingsih and I. Ramadhan, “Deciphering Political Discourse on X: A Deep Dive into SVM vs. Naïve Bayes Approaches,” in IEEE International Conference on Data Science and Engineering, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10475056/
[6] Y. Gunawan, “Sentiment Analysis of Twitter Towards the 2024 Indonesian Presidential Candidates Using the Naive Bayes Algorithms,” 2024. [Online]. Available: http://repository.ulb.ac.id/id/eprint/944
[7] R. Riyantoro and F. Fauziah, “Sentiment Analysis of Twitter Data on Indonesia’s Cabinet Using Naïve Bayes and Support Vector Machine Algorithms,” Tekno Journal, vol. 30, no. 2, 2025, [Online]. Available: https://ejournal.gunadarma.ac.id/index.php/tekno/article/view/13953
[8] G. A. Buntoro, R. Arifin, and G. N. Syaifuddiin, “The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election,” IIUM Engineering Journal, vol. 22, no. 1, pp. 45–53, 2021, [Online]. Available: https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1532
[9] W. B. Zulfikar and A. R. Atmadja, “Sentiment analysis on social media against public policy using multinomial naive bayes,” Scientific Journal of Informatics, 2023, [Online]. Available: http://shura.shu.ac.uk/31645
[10] M. N. F. Putri, H. W. Herwanto, and E. R. Wardhani, “Tinjauan Literatur Analisis Sentimen Produk E- Commerce: Dataset, Pendekatan, Metode, Dan Performa,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 3, pp. 2282–2289, Aug. 2025, doi: 10.29100/jipi.v10i3.8026.
[11] M. Siino, I. Tinnirello, and M. La Cascia, “Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers,” Inf Syst, vol. 121, Mar. 2024, doi: 10.1016/j.is.2023.102342.
[12] V. R. Joseph, “Optimal ratio for data splitting,” Stat Anal Data Min, vol. 15, no. 4, pp. 531–538, Aug. 2022, doi: 10.1002/sam.11583.
[13] S. N. Sofyan and M. Iqbal, “Analisis Sentimen Terhadap Dampak Inflasi Menggunakan Naive Bayes,” Bulletin of Information Technology (BIT), vol. 6, no. 1, pp. 1–8, 2025, doi: 10.47065/bit.v5i2.1796.
[14] A. H. Tandiano and D. Jollyta, “Classification Of Fake News In Indonesian Language Using Support Vector Machine Method,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 10, no. 2, pp. 315–322, Mar. 2024, doi: 10.33330/jurteksi.v10i2.2895.
[15] Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” Jurnal KomtekInfo, pp. 1–7, Jan. 2023, doi: 10.35134/komtekinfo.v10i1.330.
[16] R. Rinaldi and R. Goejantoro, “Penerapan Metode Klasifikasi Multinomial Naive Bayes (Studi Kasus: PT Prudential Life Samarinda Tahun 2019) Application of Naive Bayes Multinomial Classification Method (Case Study: PT Prudential Life Samarinda in 2019).”
[17] J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, “Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks,” Applied Sciences (Switzerland), vol. 13, no. 6, Mar. 2023, doi: 10.3390/app13064006.
Published
2026-01-24
How to Cite
CHRISTHOPER, Wilson; JOLLYTA, Deny; ALEXANDRO, Manuel.
Analisis Sentimen Publik Pada Platform X Menggunakan Algoritma Naïve Bayes: Isu Ijazah Presiden Jokowi.
Seminar Nasional Informatika (SENATIKA), [S.l.], p. 191-198, jan. 2026.
Available at: <https://www.ejournal.pelitaindonesia.ac.id/ojs32/index.php/SENATIKA/article/view/5483>. Date accessed: 15 feb. 2026.
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