Sentiment Analysis of the Kabur Aja Dulu Trend on X as a Basis for Designing a Public Sentiment Monitoring System Using Naïve Bayes and SVM
DOI:
https://doi.org/10.62951/ijies.v2i3.79Keywords:
Naive Bayes, Public Opinion Monitoring, Sentiment Analysis, Social Media X, SVMAbstract
Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.
References
Al-Areef, M. H., & Saputra, K. (2023). Nomor 2. 22, 270–279. Retrieved from https://ojs.trigunadharma.ac.id/index.php/jis/index
Amna, et al. (2023). Data mining. Padang: PT Global Eksekutif Teknologi.
And, I., & Expert, D. (2022). Prediksi penyakit diabetes menggunakan algoritma support vector machine (SVM). Retrieved from https://e-journal.unper.ac.id/index.php/informatics
Asrumi, Suharijadi, D. D. A., & Putri, W. D. (2023). Analisis sentimen dan penggalian opini. Jawa Tengah: Eureka Media Aksara.
Azzahra, S. A., & Wibowo, A. (2020). Analisis sentimen multi-aspek berbasis konversi ikon emosi dengan algoritme Naïve Bayes untuk ulasan wisata kuliner pada web TripAdvisor. 7(4). https://doi.org/10.25126/jtiik.202071907
Baihaqi, K. A., et al. (2023). A comparison support vector machine, logistic regression and naïve bayes for classification sentiment analysis user mobile app. International Journal of Artificial Intelligence Research, 7(1), 64. https://doi.org/10.29099/ijair.v7i1.962
Bororing, G. M. G., & Gunawan, F. (2024). Sistem pemesanan dan pembayaran makanan berbasis web terintegrasi dengan application programming interface (API). Jurnal Informatika dan Bisnis, 13(1), 37–48. https://doi.org/10.46806/jib.v13i1.1147
Hakim, Z. R., & Sugiyono, S. (2024). Analisa sentimen terhadap Kereta Cepat Jakarta–Bandung menggunakan algoritma Naïve Bayes dan K-Nearest Neighbor. Jurnal Sains dan Teknologi, 5(3), 939–945. https://doi.org/10.55338/saintek.v5i3.1423
Jelita, M. (2023). Text mining dengan topic modelling LDA dari pertanyaan gelar wicara literasi Perpustakaan Nasional RI. Media Pustakawan, 31(3), 253–265. https://doi.org/10.37014/medpus.v31i3.5237
Kahi, F. R. B., Talakua, A., & Reynaldi, R. (2024). Analisis sentimen masyarakat di Twitter terhadap pemerintahan Anies Baswedan menggunakan metode Naive Bayes classifier. Jurnal Minfo Polgan, 13(1), 324–336. https://doi.org/10.33395/jmp.v13i1.13636
Mahendro, I., & Abimanto, D. (2023). Algoritma Support Vector Machine untuk menganalisa kepuasan mahasiswa terhadap pembelajaran e-learning. Semarang: CV. Pustaka STIMART-AMNI.
Maulaya, A. K., & Junadhi. (2022). Analisis sentimen menggunakan support vector machine masyarakat Indonesia di Twitter terkait Bjorka. Jurnal CoSciTech (Computer Science and Information Technology), 3(3), 495–500. https://doi.org/10.37859/coscitech.v3i3.4358
Mulyoto, S. P. M. Si. A. (2024). Naive Bayes pada Google Colabs. Jawa Tengah: Eureka Media Aksara.
Nurtikasari, Y., Alam, S., & Hermanto, T. I. (2022). Analisis sentimen opini masyarakat terhadap film pada platform Twitter menggunakan algoritma Naive Bayes. INSOLOGI: Jurnal Sains dan Teknologi, 1(4), 411–423. https://doi.org/10.55123/insologi.v1i4.770
Pamungkas, F. S., & Kharisudin, I. (2021). Analisis sentimen dengan SVM, 4, 628–634. Retrieved from https://journal.unnes.ac.id/sju/index.php/prisma/
Pratama, R. F. P., & Maharani, W. (2025). Comparative analysis of Naive Bayes and SVM for improved emotion classification on social media. Edumatic: Jurnal Pendidikan Informatika, 9(1), 11–20. https://doi.org/10.29408/edumatic.v9i1.29087
Purnamasari, D., et al. (2023). Pengantar metode analisis sentimen. Depok: Gunadarma.
Rahman, F. N., Lestari, S., & Cipta Karya Informatika Jakarta, S. T. I. (2024). Analisis sentimen masyarakat terhadap pemerintah di era kabinet Joko Widodo berdasarkan social media X menggunakan Naïve Bayes dan K-Nearest Neighbor (KNN). Journal of Information Technology and Computer Science (INTECOMS), 7(5).
Rahman, O. H., Abdillah, G., & Komarudin, A. (2021). Klasifikasi ujaran kebencian pada media sosial Twitter menggunakan support vector machine. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(1), 17–23. https://doi.org/10.29207/resti.v5i1.2700
Safira, A., Masyarakat, A. S., & Hasan, F. N. (2023). Analisis sentimen masyarakat terhadap paylater menggunakan metode Naive Bayes classifier. Jurnal Sistem Informasi, 5(1).
Safitri, T., Umaidah, Y., & Maulana, I. (2023). Analisis sentimen pengguna Twitter terhadap BTS menggunakan algoritma support vector machine. Retrieved from http://jurnal.polibatam.ac.id/index.php/JAIC
Sitanggang, A., Umaidah, Y., Adam, R. I., & Adam, R. I. (2024). Analisis sentimen masyarakat terhadap program makan siang gratis pada media sosial X menggunakan algoritma Naïve Bayes. Jurnal Informatika dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4902
Studi, P., & Informatika, T. (2021). Analisa sentimen masyarakat Indonesia terhadap kinerja Prabowo Subianto sebagai anggota kabinet Indonesia Maju di platform Twitter menggunakan R dengan algoritma Naïve Bayes. 2(1), 1–10.
Taufiqqurrahman, H., Anggraeny, F. T., & Al Haromainy, M. M. (2023). Perbandingan algoritma Naïve Bayes dan K-Nearest Neighbor pada analisis sentimen ulasan aplikasi MyPertamina.
Yuniar, M. N. (2023). Klasifikasi kualitas air bersih menggunakan metode Naïve Bayes. Jurnal Sains dan Teknologi, 5(1), 243–246. https://doi.org/10.55338/saintek.v5i1.1383
Zuhri, K., Adha, N., & Saputri, O. (2020). Analisis sentimen masyarakat terhadap Pilpres 2019 berdasarkan opini dari Twitter menggunakan metode Naive Bayes classifier. Retrieved from https://journal-computing.org/index.php/journal-cisa/index
Zuhri, K., Adha, N., & Saputri, O. (2020). Analisis sentimen masyarakat terhadap Pilpres 2019 berdasarkan opini dari Twitter menggunakan metode Naive Bayes classifier. Retrieved from https://journal-computing.org/index.php/journal-cisa/index
Zulqornain, J. A., & Adikara, P. P. (2021). Analisis sentimen tanggapan masyarakat aplikasi TikTok menggunakan metode Naïve Bayes dan Categorial Proportional Difference (CPD). Retrieved from http://j-ptiik.ub.ac.id
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