Analisis Sentimen Publik Terhadap Deepfake AI Menggunakan Aplikasi X Dengan Metode Support Vector Machine dan Naive Bayes Classifier
- Satria Al Afif Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Ryan Randy Suryono
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
The rapid development of artificial intelligence (AI) technology has driven increased public interaction with AI-based platforms, including Deepfake AI. One of the main challenges that arises is how to objectively assess public opinion, particularly on social media, which serves as a primary medium for expressing opinions. This study aims to compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB), in analyzing public sentiment toward Deepfake AI on the X social media platform. The research dataset consists of 7,774 tweets collected between October and November 2024. After preprocessing, 5,559 tweets were used, categorized into three sentiment classes: positive, negative, and neutral. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% of the data allocated for training and 20% for testing. The results show that before applying SMOTE, the SVM algorithm achieved the highest accuracy at 71%, while Naïve Bayes only reached 62%. After the application of SMOTE, the performance of both algorithms improved, with SVM achieving 77% accuracy and Naïve Bayes reaching 68%. Thus, SVM proved to be the best-performing algorithm in this study, both before and after SMOTE application, delivering more balanced results across sentiment classes. This research demonstrates that sentiment analysis based on machine learning can be utilized to understand public opinion toward AI platforms, while also providing valuable insights for developers to improve service quality and strengthen public trust.
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References
I. Rosenberg, A. Shabtai, Y. Elovici, L. Rokach, A. D. Neural, and N. Dnns, “Supplementary Material for : Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain,” Association for Computing Machinery. vol. 54, no. 5, pp. 1–10, 2021, doi: https://doi.org/10.1145/3453158.
S. Agarwal, Q. L. Kweh, D. Jamali, W. Wider, S. F. A. Hossain, and M. A. Fauzi, “How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework,” Discover Sustainability, vol. 6, no. 1, 2025, doi: 10.1007/s43621-025-01480-7.
L. Stracqualursi and P. Agati, “Twitter users perceptions of AI-based e-learning technologies,” Sci Rep, vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-56284-y.
D. Smith-Mutegi, Y. Mamo, J. Kim, H. Crompton, and M. McConnell, “Perceptions of STEM education and artificial intelligence: a Twitter (X) sentiment analysis,” Int J STEM Educ, vol. 12, no. 9, 2025, doi: 10.1186/s40594-025-00527-5.
Katadata, “pengguna x di indonesia,” Katadata.co.id. [Online]. Available: https://search.katadata.co.id/search?q=pengguna+x+di+indonesia&source=databoks
R. Alsini, “Analysis of Real Time Twitter Sentiments using Deep Learning Models,” Journal of Applied Data Sciences, vol. 4, no. 4, pp. 480–489, 2023, doi: 10.47738/jads.v4i4.146.
Y. Akbar and T. Sugiharto, “Analisis Sentimen Pengguna Twitter di Indonesia Terhadap ChatGPT Menggunakan Algoritma C4.5 dan Naïve Bayes,” Jurnal Sains dan Teknologi, vol. 5, no. 1, pp. 115–122, 2023, doi: https://doi.org/10.55338/saintek.v4i3.1368.
Ali Ahmad and W. Gata, “Sentimen Analisis Masyarakat Indonesia di Twitter Terkait Metaverse dengan Algoritma Support Vector Machine,” Ali aJurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 6, no. 4, pp. 548–555, 2022, doi: https://doi.org/10.35870/jti k.v6i4.569.
A. Fauziah and Y. Salim, “Analisis Sentimen Pengguna X Terhadap Perkembangan Artificial Intelligence ( AI ) Menggunakan Algoritma Machine Learning,” LINER (Literatur Informatika & Komputer), vol. 1, no. 4, pp. 347–357, 2024, doi.org/10.33096/linier.v1i4.2534.
J. Saputra, L. Maryani, D. Wulandari, and W. Eka, “Analisis Performa Naive Bayes dan SVM terhadap Sentimen Teks Media Sosial dengan Word2Vec dan SMOTE,” Jurnal INSTEK, vol. 10, no. 1, pp. 143–155, 2025, doi.org/10.24269/mtkind.v17i1.7092
T. A. Zuraiyah, M. M. Mulyati, and G. H. F. Harahap, “Perbandingan Metode Naïve Bayes, Support Vector Machine Dan Recurrent Neural Network Pada Analisis Sentimen Ulasan Produk E-Commerce,” Multitek Indonesia, vol. 17, no. 1, pp. 27–43, 2023, doi: 10.24269/mtkind.v17i1.7092.
S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.
N. Amalia Putri, A. Srirahayu, and N. Arif Sudibyo, “Analisis Sentimen Terhadap Aplikasi KitaLulus Menggunakan Metode Naive Bayes dari Ulasan Google Play Store,” Smart Comp: Jurnalnya Orang Pintar Komputer, vol. 14, no. 2, pp. 269–279, 2025, doi: 10.30591/smartcomp.v14i2.7230.
R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. S. T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 8, no. 4, p. 334, 2019, doi: 10.22146/jnteti.v8i4.533.
V. A. Sulistiani and M. Hamka, “Analisis Sentimen Pengguna Media Sosial Terhadap Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine (SVM),” Journal of Information System Research (JOSH), vol. 5, no. 4, pp. 1312–1322, 2024, doi: 10.47065/josh.v5i4.5211.
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, 2024, doi: 10.58905/sana.v2i1.267.
S. N. Nugraha, R. Pebrianto, A. Latif, and M. R. Firdaus, “Analisis Sentimen Twitter Terhadap Menteri Indonesia Dengan Algoritma Support Vector Machine Dan Naive Bayes,” E-Link: Jurnal Teknik Elektro dan Informatika, vol. 17, no. 1, p. 1, 2022, doi: 10.30587/e-link.v17i1.3965.
N. V. R. Jhosefhin and C. Dewi, “Analisis Sentimen Crawling Data dari Sosial Media X tentang Gaza Menggunakan Metode SVM dan Decision Tree,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 6, no. 1, pp. 427–437, 2025, doi: 10.35870/jimik.v6i1.1225.
S. A. Azzahra and A. Wibowo, “Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi Dengan Algoritme Naïve Bayes Untuk Ulasan Wisata Kuliner Pada Web Tripadvisor,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 4, pp. 737–744, 2020, doi: 10.25126/jtiik.202071907.
A. Nadira, N. Y. Setiawan, and W. Purnomo, “Analisis Sentimen Pada Ulasan Aplikasi Mobile Banking Menggunakan Metode Naïve Bayes Dengan Kamus Inset,” Indexia, vol. 5, no. 01, p. 35, 2023, doi: 10.30587/indexia.v5i01.5138.
D. Septiani and I. Isabela, “Analisis Term Frequency Inverse Document Frequency (TF-IDF) Dalam Temu Kembali Informasi Pada Dokumen Teks,” SINTESIA: Jurnal Sistem dan Teknologi Informasi Indonesia, vol. 1, no. 2, pp. 81–88, 2022, doi.org/10.23960/jitet.v13i1.5559
Z. Hidayat and R. Kurniawan. R, “Penerapan Naive Bayes Classifier untuk Prediksi Kelayakan Penerima Bantuan Program Indonesia Pintar (PIP),” EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi, vol. 15, no. 1, p. 22, 2025, doi: 10.36448/expert.v15i1.4326.
M. R. Raihandika and R. R. Suryono, “Perbandingan Algoritma Random Forest , KNN , SVM Untuk Analisis,” Jurnal Instek Informatika Sains Dan Teknologi, vol. 6, no. 4, pp. 2403–2412, 2025, doi: 10.47065/bits.v6i4.6797.
S. F. Amrillah, D. Krisbiantoro, and A. Prasetyo, “Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 3, pp. 1281–1292, 2024, doi: 10.47065/bits.v6i3.5863.
B. Setiawan, K. Ahmad Baihaqi, E. Nurlaelasari, and H. Hikmayanti Handayani, “Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Logistic Regression dan K-Nearest Neighbor,” Technology and Science (BITS), vol. 6, no. 1, pp. 533–540, 2024, doi: 10.47065/bits.v6i1.5389.
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