Naive Bayes Method In Sentiment Analysis Of Presidential Candidates For The 2024 Election Using Python
Keywords:
Naïve Bayes, Presidential Candidates, Python, Sentiment Analysis, The 2024 Election.Abstract
The 2024 Election in Indonesia is an interesting topic for social media users. Social media has a big impact in building public political opinions, views, sentiments and preferences. Many political figures have been nominated for President based on public opinion. There are various opinions of media social users with negative, positive and neutral sentiments. However, determining the sentiment of social media users requires quite a lot of effort and time. The large number of incoming opinions regarding presidential election candidates encourages the need for methods that help to see public opinion effectively. Python is a programming language that can be used to answer these problems. By providing a standard library that is open source and has a wide range of applications in various fields. Classification will be carried out using the Naïve Bayes Classifier to determine the level of accuracy of the classification process carried out. Sentiment analysis in this research is a process carried out to find out what the results of sentiment analysis are regarding the public's response to the presidential candidates for the upcoming 2024 election and classify them into three classes using the Naïve Bayes method using Python. The results of this research showed that Python carried out sentiment analysis with the sentiment percentage results for candidate Anies Muhaimin with a positive class of 64.91%, neutral 28.07% and negative 7.02% with a Naïve Bayes accuracy value of 75%. For candidate Prabowo Gibran, the positive class is 12.38%, neutral 6.67% and negative 80.95% with a Naïve Bayes accuracy value of 81%. Meanwhile, the candidate Ganjar Mahfud has a positive class of 40%, neutral 50.67% and negative 9.33% with a Naïve Bayes accuracy value of 60%. So that we can identify public opinion about presidential candidates for the 2024 election using the Naïve Bayes method using Python.
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References
Aggrawal, N. (2018). Detection of Offensive Tweets: A Comparative Study. Computer Reviews Journal, Vol 1 No 1, 75–89.
Asshiddiqie, J. (2007). Pokok-Pokok Hukum Tata Negara Indonesia Pasca Reformasi (xviii). PT Bhuana Ilmu Populer. https://simpus.mkri.id/opac/detail-opac?id=6455
Bhatia, S., Sharma, M., & Bhatia, K. K. (2018). Sentiment Analysis and Mining of Opinions. Studies in Big Data, 30(May), 503–523. https://doi.org/10.1007/978-3-319-60435-0_20
Cambria, E., Poria, S., Bisio, F., Bajpai, R., & Chaturvedi, I. (2015). The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis. Springer International Publishing Switzerland, 3–22. https://www.researchgate.net/publication/273635373_The_CLSA_Model_A_Novel_Framework_for_Concept-Level_Sentiment_Analysis
Chadha, N., Gangwar, R. C., & Bedi, R. (2015). Current Challenges and Application of Speech Recognition Process using Natural Language Processing: A Survey. International Journal of Computer Applications, 131(11), 28–31. https://doi.org/10.5120/ijca2015907471
Coletta, L. F. S., Ponti, M., Hruschka, E. R., Acharya, A., & Ghosh, J. (2019). Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing, 358(September), 150–165. https://doi.org/10.1016/j.neucom.2019.04.070
Firdaus, A., & Firdaus, W. I. (2021). Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi : (Sebuah Ulasan). Jurnal JUPITER, 13(1), 66.
Fitriyyah, S. N. J., Safriadi, N., & Pratama, E. E. (2019). Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(3), 279. https://doi.org/10.26418/jp.v5i3.34368
H, A. T. J. (2015). Preprocessing Text untuk Meminimalisir Kata yang Tidak Berarti dalam Proses Text Mining. Informatika UPGRIS, 1, 1–9.
Hidayatullah, A. F., Aulia, A., Yusuf, F., Juwairi, K. P., Abida, R., & Nayoan, N. (2019). Identifikasi konten kasar pada tweet bahasa Indonesia. Jurnal Linguistik Komputasional, 2(1), 1–5. http://inacl.id/journal/index.php/jlk/article/view/15
Indrayuni, E., & Wahyudi, M. (2015). Penerapan Charachter N-Gram Untuk Sentiment Review Hotel Menggunakan Algoritma Naive Bayes. Konfrensi Nasional Ilmu Pengetahuan Dan Teknologi (KNIT), 88–93.
Istiani, N., & Islamy, A. (2020). Fikih Media Sosial Di Indonesia. Asy Syar’Iyyah: Jurnal Ilmu Syari’Ah Dan Perbankan Islam, 5(2), 202–225. https://doi.org/10.32923/asy.v5i2.1586
Jailani. (2015). Sistem Demokrasi di Indonesia Ditinjau dari Sudut Hukum Ketatanegaraan. Inovatif: Jurnal Ilmu Hukum, Vol. 8, No., 8(1), 134–147.
Lukmana, D. T., Subanti, S., & Susanti, Y. (2019). Analisis Sentimen Terhadap Calon Presiden 2019 Dengan Support Vector Machine Di Twitter. Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019 UMT, 2002, 154–160.
Mahbubah, L. D., & Zuliarso, E. (2019). Analisa Sentimen Twitter Pada Pilpres 2019 Menggunakan Algoritma Naive Bayes. Sintak, 194–195. https://www.unisbank.ac.id/ojs/index.php/sintak/article/view/7585
Nurrun Muchammad Shiddieqy, H., Paulus Insap, S., & Wing Wahyu, W. (2016). Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen Di Twitter. Seminar Nasional Teknologi Informasi Dan Komunikasi, 2016(March), 57–64.
Pasek, P., Mahawardana, O., Arya, G., Agus, I. P., & Pratama, E. (2022). Analisis Sentimen Berdasarkan Opini dari Media Sosial Twitter terhadap “ Figure Pemimpin ” Menggunakan Python. JITTER-Jurnal Ilmiah Teknologi Dan Komputer, 3(1).
Pratama, S. F., Andrean, R., & Nugroho, A. (2019). Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode Fined-Grained Sentiment Analysis. JOINTECS (Journal of Information Technology and Computer Science), 4(2), 39. https://doi.org/10.31328/jointecs.v4i2.1004
Subiyanto, A. E. (2020). Pemilihan Umum Serentak yang Berintegritas sebagai Pembaruan Demokrasi Indonesia. Jurnal Konstitusi, 17(2), 355. https://doi.org/10.31078/jk1726
Wibowo, W., Ulama, B. S. S., & Azies, H. Al. (2020). Modul-Prokom-DSB-versi-18-agustus-BW-compressed_2.pdf (p. 135).
