Sentiment Analysis of Public Opinion on Danantara Via Social Media X
DOI:
https://doi.org/10.58982/ha2x2j34Keywords:
Sentiment Analysis; DANANTARA; Social Media; Naïve Bayes; Web Scraping;Abstract
Social media has become a primary channel for the public to express opinions on various issues, including public services. One topic that has been widely discussed on platform X is Daya Anagata Nusantara (DANANTARA), a government-owned strategic investment management agency. Public sentiment toward DANANTARA is divided into three categories—positive, negative, and neutral—which may influence broader public perception. This study aims to analyze public sentiment toward DANANTARA using data obtained from platform X. The methods employed include web scraping for automated data collection and the Naïve Bayes algorithm for classifying sentiment into three labels: positive, negative, and neutral. Platform X was selected because it has over 19.5 million active users in Indonesia, making it a representative data source. From the 930 data points analyzed, it was found that public sentiment toward DANANTARA is predominantly neutral. This research is expected to provide an objective overview of public perception regarding DANANTARA.
References
[1] A. Sentimen and D. A. N. Dana, “Twitter Dengan Metode Naive Bayes Classifier,” vol. 8, no. 5, pp. 9858–9864., 2024.
[2] M. I. Ghozali, W. H. Sugiharto, and A. F. Iskandar, “Analisis Sentimen Pinjaman Online Di Media Sosial Twitter Menggunakan Metode Naive Bayes,” KLIK Kaji. Ilm. Inform. Dan Komput., vol. 3, no. 6, pp. 1340–1348, 2023.
[3] R. Abdul et al., “Analisis Sentimen Masyarakat Terhadap Undang-Undang Perlindungan Data Pribadi Pada Aplikasi X Dengan Metode Support Vector Machine,” J. Method., vol. 10, no. 1, pp. 6–11, 2024.
[4] F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” vol. 6, no. 9, pp. 4305–4313, 2022.
[5] H. Syah and A. Witanti, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM),” J. Sist. Inf. dan Inform., vol. 5, no. 1, pp. 59–67, 2022.
[6] S. V. Sari, “Analisis Sentimen Pelanggan Toko Online Jd . Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” vol. 10, no. 2, pp. 681–686, 2019.
[7] N. Fadhilla, R. Afrianti, and A. Badawi, “Web Scraping Senyawa Herbal di Indonesia Menggunakan Selenium Python,” vol. 4307, no. 4, pp. 1362–1366, 2024.
[8] V. D. Lestari, J. Fredricka, and F. I. Nizar, “Implementasi Metode Bayes untuk Klasifikasi Penyakit Pasien pada Puskesmas Anggut Atas Kota Bengkulu,” Digit. Transform. Technol., vol. 5, no. 1, pp. 409–419, 2025.
[9] B. Rahmatullah, S. A. Saputra, P. Budiono, and D. P. Wigandi, “Sentiment Analysis of Free Nutritious Meals Using Naive Bayes Algorithm,” J. Inf. Technol., vol. 5, no. 1, pp. 259–264, 2025.
[10] L. Hermawan and M. B. Ismiati, “Pembelajaran Text Preprocessing Berbasis Simulator Untuk Mata Kuliah Information Retrieval,” J. Transform., vol. 17, no. 2, pp. 188–199, 2022.
[11] J. A. Wibowo, V. C. Mawardi, and T. Sutrisno, “Visualisasi Word Cloud Hasil Analisis Sentimen Berbasis,” J. Serina Sains, Tek. dan Kedokt., vol. 2, no. 1, pp. 61–70, 2024.
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