TWITTER SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL DISPUTE DECISION USING NAÏVE BAYES AND SVM

Ihsan Aulia Rahman; Nanang Ruhyana

doi:10.34288/jri.v7i1.355

Authors

Ihsan Aulia Rahman Universitas Nusa Mandiri
Nanang Ruhyana Universitas Nusa Mandiri

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v7i1.355

Keywords:

Sentiment Analysis, Twitter, 2024 Presidential Election Dispute, Naïve Bayes, RapidMiner

Abstract

Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision

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References

Alruily, M., & Shahin, O. R. (2020). Sentiment Analysis of Twitter Data for Saudi Universities. International Journal of Machine Learning and Computing, 10(1), 18–24. https://doi.org/10.18178/ijmlc.2020.10.1.892

Aripiyanto, S., Tukino, T., Sufyan, A., & Nandaputra, R. (2022). Sentimen Analisis Twitter Ibu Kota Negara Nusantara Menggunakan Long Short-Term Memory dan Lexicon Based. EXPERT: Jurnal Manajemen Sistem Informasi Dan Teknologi, 12(2), 119. https://doi.org/10.36448/expert.v12i2.2821

Audrey, O., Ratnawati, D. E., & Arwani, I. (2022). Analisis Sentimen Pengguna Twitter Terhadap Opini Non Fungible Token di Indonesia Menggunakan Algoritma Random Forest Classifier. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(12), 5889–5897. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12035%0Ahttp://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/12035/5333

Berliana, D. R., & Santoso, B. (2022). ELEKTABILITAS RIDWAN KAMIL DAN ANIES BASWEDAN DALAM SIMULASI PILPRES 2024 DI TWITTER (ANALISIS JARINGAN MEDIA SOSIAL DAN ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP #RIDWANKAMIL DAN #ANIESBASWEDAN). Mediakom : Jurnal Ilmu Komunikasi, null, null. https://doi.org/10.35760/mkm.2022.v6i2.6962

Darmawan, R., Indra, I., & Surahmat, A. (2022). Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru di Twitter. Jurnal Kajian Ilmiah, 22(2), 143–152. https://doi.org/10.31599/jki.v22i2.1130

Fahmi, R. N., Nursyifa, N., & Primajaya, A. (2021). Analisis Sentimen Pengguna Twitter Terhadap Kasus Penembakan Laskar Fpi Oleh Polri Dengan Metode Naive Bayes Classifier. JIKO (Jurnal Informatika Dan Komputer), 5(2), 61–66. https://ejournal.akakom.ac.id/index.php/jiko/article/view/437/0

Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm. Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932

Fremmuzar, P., & Baita, A. (2023). Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter. Komputika : Jurnal Sistem Komputer, 12(2), 57–66. https://doi.org/10.34010/komputika.v12i2.9460

Hartanto. (2017). Hartanto 2017 text mining dan sentimen analisis twitter. Jurnal Psikologi Ilmiah, 9(1), 18–25. https://journal.unnes.ac.id/nju/index.php/INTUISI/article/view/9561%0Ahttps://journal.unnes.ac.id/nju/index.php/INTUISI/article/viewFile/9561/6187

Izzati, A. N. (2024). Analisis Sentimen Hasil Putusan Mk Terkait Sengketa Pilpres 2024 Pada Media Sosial X. Jurnal Teknologi Informasi Indonesia (JTII), 9(1), 43–50. https://doi.org/10.30869/jtii.v9i1.1338

Kurniawan, I., & Susanto, A. (2019). Implementasi Metode K-Means dan Naïve Bayes Classifier untuk Analisis Sentimen Pemilihan Presiden (Pilpres) 2019. Eksplora Informatika, 9(1), 1–10. https://doi.org/10.30864/eksplora.v9i1.237

Maulana, N. A., & Darwis, D. (2025). Perbandingan Metode SVM dan Naïve Bayes untuk Analisis Sentimen pada Twitter tentang Obesitas di Kalangan Gen Z Sistem Informasi , Fakultas Teknik dan Ilmu Komputer , Universitas Teknokrat Indonesia , Indonesia Comparison of SVM and Naive Bayes Methods fo. 5(3), 655–666.

Naraswati, N. P. G., Nooraeni, R., Rosmilda, D. C., Desinta, D., Khairi, F., & Damaiyanti, R. (2021). Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification. Sistemasi, 10(1), 222. https://doi.org/10.32520/stmsi.v10i1.1179

Pahtoni, T. Y., & Jati, H. (2024). Analisis Sentimen Data Twitter Terkait Chatgpt Menggunakan Orange Data Mining. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(2), 329–336. https://doi.org/10.25126/jtiik.20241127276

Pakpahan, I., & Jasman Pardede. (2023). Analisis Sentimen Penanganan Covid-19 Menggunakan Metode Long Short-Term Memory Pada Media Sosial Twitter. Jurnal Publikasi Teknik Informatika, 2(1), 12–25. https://doi.org/10.55606/jupti.v1i1.767

Putranti, N. D., & Winarko, E. (2014). Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 8(1), 91. https://doi.org/10.22146/ijccs.3499

Rai, S., Goyal, S. B., & Kumar, J. (2021). Sentiment Analysis of Twitter Data. International Research Journal on Advanced Science Hub, 2, 56–61. https://doi.org/10.47392/irjash.2020.261

Rezwanul, M., Ali, A., & Rahman, A. (2017). Sentiment Analysis on Twitter Data using KNN and SVM. International Journal of Advanced Computer Science and Applications, 8(6), 19–25. https://doi.org/10.14569/ijacsa.2017.080603

Ruhyana, N. (2019). Analisis Sentimen terhadap Penerapan Sistem Plat Nomor Gnajil/Genap pada Twitter dengan Metode Klasifikasi Naive Bayes. Jurnal IKRA-ITH Informatika, 3(1), 94–99. www.situs.com

Sandha, S. S., Cabrera, W., Al-Kateb, M., Nair, S., & Srivastava, M. (2018). In-database distributed machine learning: Demonstration using Teradata SQL engine. Proceedings of the VLDB Endowment, 12(12), 1854–1857. https://doi.org/10.14778/3352063.3352083

Sandryan, M. K., Rahayudi, B., & Ratnawati, D. E. (2021). Analisis Sentimen Pada Media Sosial Twitter Terhadap Undang-Undang Cipta Kerja Menggunakan Algoritma Backpropagation dan Term Frequency-Inverse Document Frequency. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(12), 5349–5355.

Saputra, F. T., Nurhadryani, Y., Wijaya, S. H., & Defina, D. (2021). Analisis Sentimen Bahasa Indonesia pada Twitter Menggunakan Struktur Tree Berbasis Leksikon. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(1), 135. https://doi.org/10.25126/jtiik.0814133

Sudiantoro, A. V., & Zuliarso, E. (2018). Analisis Sentimen Twitter Menggunakan Text Mining Dengan Algoritma Naïve Bayes Classifier. Prosiding SINTAK 2018, 398–401.

Taufiqi, A. M., & Nugroho, A. (2023). Sentimen Pengguna Twitter Mengenai Isu Kebocoran Data Dengan Algoritma Naïve Bayes. Jurnal Nasional Ilmu Komputer, 4(1), 1–11. https://doi.org/10.47747/jurnalnik.v4i1.1091

Vonega, D. A., Fadila, A., & Kurniawan, D. E. (2022). Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024. Journal of Applied Informatics and Computing, 6(2), 129–135. https://doi.org/10.30871/jaic.v6i2.4300

Zen, B. P., Wicaksana, D., & Alfidzar, H. (2022). ANALISIS SENTIMEN TWEET VAKSIN COVID 19 SINOVAC MENGGUNAKAN METODE SUPPORT VECOR MACHINE. Jurnal Data Mining Dan Sistem Informasi, null, null. https://doi.org/10.33365/jdmsi.v3i2.1926