Analisis Sentimen pada Media Sosial dengan Teknik Kecerdasan Buatan Naïve Bayes: Kajian Literatur Review

Amirulah Kaharudin; Ari Agus Supriyadi; Muhlis; Haqun Baitika; Muhamad Derryanur

Authors

Amirulah Kaharudin Universitas Pamulang
Ari Agus Supriyadi Universitas Pamulang
Muhlis Universitas Pamulang
Haqun Baitika Universitas Pamulang
Muhamad Derryanur Universitas Pamulang

Keywords:

Social Media, Artificial Intelligence, Literature Review, The Naïve Bayes Method

Abstract

This study discusses sentiment analysis on social media using Naïve Bayes artificial intelligence (AI) techniques. This study aims to provide a literature review on AI techniques used in sentiment analysis on social media. The data collection used in this study was to search for related articles from various literary sources using the Publish or Perish application (https://harzing.com/resources/publish-or-perish). There are 8 articles used in this study, which relate to sentiment analysis on social media with the use of artificial intelligence techniques. The method used in this research is a literature study by collecting information from relevant journal articles. Based on the results and discussion of this study, it can be concluded that the use of artificial intelligence techniques in sentiment analysis on social media can provide great benefits in processing large amounts of data in a relatively short time, as well as recognizing certain patterns in the data. However, the use of artificial intelligence techniques in sentiment analysis on social media also has challenges, such as issues of accuracy, privacy and ethics. The implications of sentiment analysis on social media with AI techniques is a rapidly growing field of research and has various benefits for various sectors. Therefore, further research and development of AI technology is expected to help improve the quality of human life.

References

Adi, S., Wulandari, M., Kemala Mardiana, A., & Muzakki, A. (2018). Survei: Topik dan Tren Analisis Sentimen pada Media Online. Seminar Nasional Teknologi Informasi Dan Multimedia 2018 (Universitas Amikom Yogyakarta), 55–60.

Adillah, T. M., Alkhalifi, Y., Mayangky, N. A., & Gata, W. (2020). Analisis Sentimen Opini Publik Mengenai Larangan Mudik pada Twitter Menggunakan Naive Bayes. Jurnal CorelT, 6(2), 85–88.

Bagus Sasmita, A., Rahayudi, B., & Muflikhah, L. (2022). Analisis Sentimen Komentar pada Media Sosial Twitter tentang PPKM Covid-19 di Indonesia dengan Metode Naïve Bayes (Vol. 6, Issue 3). http://j-ptiik.ub.ac.id

Dwiraswati, O., & Siregar, N. K. (2019). Analisis Sentimen pada Twitter terhadap Penggunaan Antibiotik di Indonesia dengan Naive Bayes Classifier. Media Informasi, 15(1), 1–9. www.search.twitter.com

Hasibuan, F., Priatna, W., & Sri Lestari, T. (2022). Analisis Sentimen terhadap Kementrian Perdagangan pada Sosial Media Twitter Menggunakan Metode Naïve Bayes. Techno.COM, 21(4), 741–752.

Juanita, S. (2020). Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes. Jurnal Media Informatika Budidarma, 4(3), 552. https://doi.org/10.30865/mib.v4i3.2140

Permana, A. A., Fahrezi, M. F., Priyanggodo, D. Y., Kristiyanti, D. A., & Sihotang, M. (2021). Sentimen Analisis Opini Masyarakat pada Media Sosial Twitter terhadap Vaksin Berbayar Menggunakan Metode Naïve Bayes Classifier (NBC). JTS: Jurnal Teknik, 10(2), 84–92. http://jurnal.umt.ac.id/index.php/jt/index

Permatasari, P. A., Linawati, L., & Jasa, L. (2021). Survei Tentang Analisis Sentimen Pada Media Sosial. Majalah Ilmiah Teknologi Elektro, 20(2), 177. https://doi.org/10.24843/mite.2021.v20i02.p01

Pratiwi, E. O. I., & Wiyli, Y. (2021). Analisis Sentimen Kualitas Layanan Teknologi Pembayaran Elektronik pada Twitter (Studi Kasus Ovo dan Dana). JEISBI (Journal of Emerging Information Systems and Business Intelligence), 2(3), 47–54.

Septiana, R. D., Agung, B. S., & Tukiyat. (2021). Analisis Sentimen Vaksinasi Covid-19 pada Twitter Menggunakan Naive Bayes Classifier dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization. Jurnal Sistem Komputer Dan Kecerdasan Buatan, 5(1), 49–56.

Taufik, I., & Pamungkas, S. A. (2018). Analisis Sentimen terhadap Tokoh Publik Menggunakan Algoritma Support Vector Machine (SVM). Jurnal “LOGIKA”, 8(1), 69–79.

Zalukhu, P. S., Handhayani, T., & Sitorus, M. (2023). Analisis Sentimen terhadap Kenaikan BBM di Indonesia pada Media Sosial Twitter Menggunakan Metode Naïve Bayes. Jurnal Sistem Informasi Teknik Komputer, 8(1), 65–69.

Zusrotun, O. P., Murti, A. C., & Fiati, R. (2022). Analisis Sentimen Terhadap Belajar Online pada Media Sosial Twitter Menggunakan Algoritma Naive Bayes. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 11(3), 310–319. https://doi.org/10.23887/janapati.v11i3.49160

Jupri, G. D., Rosandi, & Perani Rosyani. (2022). Implementasi Artificial Intelligence Pada Sistem

Manufaktur Terintegrasi: Implementasi Artificial Intelligence. BISIK: Jurnal Ilmu Komputer, Hukum, Kesehatan Dan Sosial Humaniora, 1(2), 140–143. Retrieved from https://journal.mediapublikasi.id/index.php/bisik/article/view/341.