Analisis Sentimen Publik di Twitter Pasca Debat Kelima Pilpres 2024 dengan Naive Bayes

Authors

  • Anjana Haya Atha Zharifa Program Studi Informatika, Universitas Teknologi Yogyakarta
  • Erik Iman Heri Ujianto Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta

DOI:

https://doi.org/10.29408/edumatic.v8i2.28048

Keywords:

naïve bayes classifier, presidential election, public opinion, sentiment analysis, twitter

Abstract

The presidential election in Indonesia is a frequently discussed topic on social media, especially Twitter. This platform provides a space for people to express their views on presidential candidates and election issues, making it suitable as a data source for this study. This study aims to analyze public sentiment towards presidential election news on Twitter using the Naïve Bayes Classifier method. Data was taken from Twitter for the period 5–13 February 2024 with a total of 2,561 comments. The research process includes data collection, preprocessing, data labeling, and model training and testing. Naïve Bayes was chosen because it is efficient in text classification and has several variants for model experiments. Sentiment is classified into three main categories, namely positive, negative, and neutral. The results showed that negative comments dominated (41%), followed by positive (37.3%) and neutral (21.7%). The Multi Naïve Bayes Classifier model provided the highest accuracy (81%), followed by Bernoulli Naïve Bayes (80%) and Gaussian Naïve Bayes (76%). This difference in accuracy is influenced by the model's sensitivity to data characteristics, such as the number of features and sentiment distribution. This research has the potential to help campaign teams understand the issues that trigger negative responses and support policy makers in designing more effective political communication strategies.

References

Afriansyah, M., Saputra, J., Ardhana, V. Y. P., & Sa’adati, Y. (2024). Algoritma Naive Bayes Yang Efisien Untuk Klasifikasi Buah Pisang Raja Berdasarkan Fitur Warna. Journal of Information Systems Management and Digital Business (JISMDB), 1(2), 236–248. https://doi.org/10.59407/jismdb.v1i2.438

Ardiansyah, M., Sunyoto, A., & Luthfi, E. T. (2021). Analisis Perbandingan Akurasi Algoritma Naïve Bayes dan C4.5 untuk Klasifikasi Diabtes. Edumatic: Jurnal Pendidikan Informatika, 5(2), 147-156. https://doi.org/10.29408/edumatic.v5i2.3424

Febriyani, E., & Februariyanti, H. (2023). Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter. Jurnal Tekno Kompak, 17(1), 25–38. https://doi.org/10.33365/jtk.v17i1.2061

Fudholi, L. A., Rahaningsih, N., & Dana, R. D. (2024). Sentimen Analisis Perilaku Penggemar Coldplay Di Media Sosial Twitter Menggunakan Metode Naive Bayes. Jurnal Mahasiswa Teknik Informatika, 8(3), 4150–4159. https://doi.org/10.36040/jati.v8i3.9827

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

Haq, M. Z., Octiva, C. S., Ayuliana, A., Nuryanto, U. W., & Suryadi, D. (2024). Algoritma Naïve Bayes untuk Mengidentifikasi Hoaks di Media Sosial. Jurnal Minfo Polgan, 13(1), 1079–1084. https://doi.org/10.33395/jmp.v13i1.13937

Laorensa, E., Suri, E. W., & Dani, R. (2024). Peran Media Sosial Dalam Membentuk Persepsi Pemilih Pada Pemilu 2024 (Studi Di Kabupaten Bengkulu Tengah). Jurnal Ilmiah Administrasi Publik, 2(1), 1–16.

Makarawung, Y. A., Wulandari, Y. F., & Himawan, S. (2024). Analisis Konten TikTok dalam Komunikasi Politik Capres- Cawapres di Pemilu 2024 untuk Generasi Z. 3(4), 320–336.

Noorikhsan, F. F., Ramdhani, H., Sirait, B. C., & Khoerunisa, N. (2023). Dinamika Internet, Media Sosial, dan Politik di Era Kontemporer: Tinjauan Relasi Negara-Masyarakat. Journal of Political Issues, 5(1), 95–109. https://doi.org/10.33019/jpi.v5i1.131

Perdana, A., Hermawan, A., & Avianto, D. (2022). Analisis Sentimen Terhadap Isu Penundaan Pemilu di Twitter Menggunakan Naive Bayes Clasifier. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 11(2), 195–200. https://doi.org/10.32736/sisfokom.v11i2.1412

Putri, D. D., Nama, G. F., & Sulistiono, W. E. (2022). Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Informatika Dan Teknik Elektro Terapan, 10(1), 34–40. https://doi.org/10.23960/jitet.v10i1.2262

Rahayu, D. D., Fatchan, M., & Ligouri, A. (2024). Analisis Sentimen Twitter Terpilihnya Prabowo - Gibran Menggunakan Metode Neural Network. Jurnal Tematik, 11(1), 85–91. https://doi.org/10.38204/tematik.v11i1.1943

Retnosari, R. (2021). Analisis Kelayakan Kredit Usaha Mikro Berjalan Pada Perbankan Dengan Metode Naive Bayes. Jurnal PROSISKO, 8(1), 53–59.

Salsabila, S. M., Murtopo, A. A., & Fadhilah, N. (2022). Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier. Jurnal Manajemen Informatika Politeknik Ganesha, 11(2), 30–35. https://doi.org/10.33395/jmp.v11i2.11640

Sarimole, F. M., & Kudrat. (2024). Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine. Jurnal Sains Dan Teknologi, 5(3), 783–790. https://doi.org/10.55338/saintek.v5i1.2702

Setiyawati, D., & Cahyono, N. (2023). Analisa Sentimen Pengguna Sosial Media Twitter Terhadap Perokok di Indonesia. Indonesian Journal of Computer Science Attribution, 12(1), 262–272. https://doi.org/10.33022/ijcs.v12i1.3154

Sihombing, L. O., Hannie, H., & Dermawan, B. A. (2021). Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier. Edumatic: Jurnal Pendidikan Informatika, 5(2), 233-242. https://doi.org/10.29408/edumatic.v5i2.4089

Singgalen, Y. A. (2022). Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier. Building of Informatics, Technology and Science (BITS), 4(3), 1343–1352. https://doi.org/10.47065/bits.v4i3.2486

Solihin, F., Awaliyah, S., & Shofa, A. M. A. (2021). Pemanfaatan Twitter Sebagai Media Penyebaran Informasi Oleh Dinas Komunikasi dan Informatika. Jurnal Pendidikan Ilmu Pengetahuan Sosial (JPIPS), 1(13), 52–58.

Sunata, M. H. A., Irwiensyah, F., & Hasan, F. N. (2024). Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE. Jurnal Media Informatika Budidarma, 8(3), 1313-1322. https://doi.org/10.30865/mib.v8i3.7708

Verawati, I., & Audit, B. S. (2022). Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By. u. Jurnal Media Informatika Budidarma, 6(3), 1411-1417. https://doi.org/10.30865/mib.v6i3.4132

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Published

2024-12-19

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Vol. 8 No. 2 (2024): Edumatic: Jurnal Pendidikan Informatika

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