Analisis Sentimen Terhadap Presidensi G20 2022 pada Media Sosial Twitter Menggunakan Metode Naïve Bayes


Authors

  • I Gusti Agung Indrawan Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Dewa Ayu Indah Cahya Dewi Politeknik Negeri Bali, Badung, Indonesia
  • Ida Ayu Putu Ananda Wisdantini Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia

DOI:

https://doi.org/10.30865/klik.v4i1.1104

Keywords:

KTT G20; Twitter; Naive Bayes; Sentiment Analysis

Abstract

Twitter is one of the social media as a suitable forum to express opinions from the community today. Many people express their opinions through posts on social media Twitter on issues that are trending topics. One of the trending topics in 2022 is the implementation of the G20 presidency held in Bali, Indonesia. This issue, can generate positive or negative opinions from the community. In the research, a sentiment analysis will be carried out on the implementation of G20 presidency activities in 2022 using Python Google Colab, RapidMiner Studio, and Orange Data Mining. In the initial data collection, there are 24,840 data that will go through the stages of text pre-processing, labeling, sharing, and data classification using the Naïve Bayes classification method. So that it obtained the results of the classification of positive sentiment 1,600 data (72.37%) and negative sentiment 611 data (27.63%). Based on the results of the sentiment classification, it can be concluded that the public supports the implementation of G20 activities in 2022 in Bali, Indonesia, seen from positive sentiment more than negative sentiment. The Naïve Bayes classification has a fairly good performance in classifying the topics studied, where an accuracy value of 88.01% is obtained.

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References

A. F. P. I. K. M. I. A. S. Putri, “PENGARUH IMPLEMENTASI LEADERSHIP INDONESIA TERHADAP PRESIDENSI G20 DAN PERTUMBUHAN EKONOMI DI BALI,” vol. 8, no. 2, pp. 276–291, 2022.

N. L. I. Sukawiyana Agus, Anak Agung Elik Astari, “AKTUALISASI PEREKONOMIAN INDONESIA DALAM PRESIDENSI G20 PELUANG BERTUMBUH ATAU TREN? (STUDI ANALISIS EKONOMI),” Indones. Perspect., vol. 7, no. 1, pp. 196–218, 2022, doi: 10.14710/ip.v7i1.48596.

A. Sasmito Aribowo, “Analisis Sentimen Publik pada Program Kesehatan Masyarakat menggunakan Twitter Opinion Mining,” Semin. Nas. Inform. Medis, vol. 0, no. 0, pp. 17–23, 2018, [Online]. Available: https://journal.uii.ac.id/snimed/article/view/11877

E. Indrayuni, “Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 29–36, 2019, doi: 10.31294/jki.v7i1.1.

W. Yulita et al., “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” Jdmsi, vol. 2, no. 2, pp. 1–9, 2021.

M. Syarifuddinn, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020, doi: 10.33480/inti.v15i1.1347.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.

A. N. Ulfah and M. K. Anam, “Analisis Sentimen Hate Speech Pada Portal Berita Online Menggunakan Support Vector Machine (SVM),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 1, pp. 1–10, 2020, doi: 10.35957/jatisi.v7i1.196.

M. Syarifuddinn, “Analisis Sentimen Opini Publik Terhadap Efek Psbb Pada Twitter Dengan Algoritma Decision Tree,Knn, Dan Naïve Bayes,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 87–94, 2020, doi: 10.33480/inti.v15i1.1433.

D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon: Sentiment Analysis of Online Lectures in Indonesia from Twitter Dataset Using InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021.

R. W. Samsir, Ambiyar, Unung Verawardina, Firman Edi, “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, no. 1, p. 149, 2021, doi: 10.30865/mib.v5i1.2604.

D. P. Santoso and W. Wibowo, “Analisis Sentimen Ulasan Aplikasi Buzzbreak Menggunakan Metode Naïve Bayes Classifier pada Situs Google Play Store,” J. Sains dan Seni ITS, vol. 11, no. 2, 2022, doi: 10.12962/j23373520.v11i2.72534.

E. S. Negara, R. Andryani, and P. H. Saksono, “Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial,” J. INKOM, vol. 10, no. 1, p. 27, 2016, doi: 10.14203/j.inkom.433.

A. Wandani, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 651–665, 2021.

D. Alita and A. R. Isnain, “Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier,” J. Komputasi, vol. 8, no. 2, pp. 50–58, 2020, doi: 10.23960/komputasi.v8i2.2615.

Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.

W. R. D. Astuti, “Kerja Sama G20 dalam Pemulihan Ekonomi Global dari COVID-19,” Andalas J. Int. Stud., vol. 9, no. 2, p. 131, 2020, doi: 10.25077/ajis.9.2.131-148.2020.

J. Eka Sembodo, E. Budi Setiawan, and Z. Abdurahman Baizal, “Data Crawling Otomatis pada Twitter,” no. September, pp. 11–16, 2016, doi: 10.21108/indosc.2016.111.

A. F. Harismawan, “Analisis Perbandingan Performa Web Service Menggunakan Bahasa Pemrograman Python , Php ,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 1, pp. 237–245, 2017, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j- ptiik/article/view/781

M. L. Suliztia, “Penerapan Analisis Random Forest pada Prototype Sistem Prediksi Harga Kamera Bekas Menggunakan Flask,” Fak. Mat. Dan Ilmu Pengetah. Alam, pp. 1–107, 2020.


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Published: 2023-08-30
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