SENTIMENT ANALYSIS ON THE PERMENDIKBUD CONCERN PREVENTION AND TREATMENT OF SEXUAL VIOLENCE IN HIGHER EDUCATIONAL ENVIRONMENTS USING SUPPORT VECTOR MACHINE (SVM)
Abstract
Social media is no longer a foreign thing for people in today's technological era, one of the social media that is often used is Twitter. Twitter is used to communicate with other people and Twitter users can also give each other opinions on an issue. By involving 1252 Tweets, this study aimed to use the Support Vector Machine (SVM) algorithm on Tweet data. The processes carried out in this research are crawling, cleaning, translate, labeling, tokenizing, stop words, stemming, SVM classification. .The results showed that the accuracy level of using the SVM algorithm after the param grid was 80.3% using the parameter C = 10; gamma = 0.1; and kernel = rbf as a benchmark in the classification process. This shows that the classification process using the SVM algorithm is quite accurate.
Downloads
References
Statista.com, “Most popular social networks worldwide as of October 2021, ranked by number of active users,” Statista, 2021. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.
Z. Bokaee Nezhad and M. A. Deihimi, “Twitter sentiment analysis from Iran about COVID 19 vaccine,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 16, no. 1, p. 102367, 2021, doi: 10.1016/j.dsx.2021.102367.
M. Perez-Cepeda and L. G. Arias-Bolzmann, “Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter,” J. Bus. Res., no. xxxx, 2021, doi: 10.1016/j.jbusres.2021.11.008.
Statita.com, “Statista.com,” Leading countries based on number of Twitter users as of October 2021, 2021. https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/ (accessed Dec. 12, 2021).
D. S. Utami and A. Erfina, “Analisis Sentimen Pinjaman Online Di Twitter Menggunakan Algoritma Support Vector Machine (Svm),” Semin. Nas. Sist. Inf. …, pp. 299–305, 2021, [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/34.
B. Amal, U. Nahdlatul, U. Indonesia, and K. J. Pusat, “Tinjauan Hukum Terhadap Frasa ‘ Tanpa Persetujuan Korban ’ Dalam Permendikbud Nomor 30 Tahun 2021 Tentang,” J. CREPIDO J. Mengenai Dasar-Dasar Pemikir. Huk. Filsafat dan Ilmu Huk., vol. 03, no. November, pp. 86–95, 2021, doi: https://doi.org/10.14710/crepido.3.2.86-95.
F. Lustyantie, N. & Arung, Pembelajaran Inovatif Bahasa dan Sastra: Mengembalikan Prinsip Bahasa dan Sastra sebagai Cara Berpikir Inovatif. Yogyakarta: Penerbit Deepublish, 2020.
M. Alam, “Reconstructing anti-capitalism as heterodoxa in Indonesia’s youth-led urban environmentalism Twitter account,” Geoforum, vol. 114, pp. 151–158, 2020, doi: 10.1016/j.geoforum.2020.06.005.
A. R. Rahmanti, D. N. A. Ningrum, L. Lazuardi, H. C. Yang, and Y. C. Li, “Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the ‘New Normal’ During the COVID-19 Pandemic in Indonesia,” Comput. Methods Programs Biomed., vol. 205, p. 106083, 2021, doi: 10.1016/j.cmpb.2021.106083.
P. L. Liu, “COVID-19 information on social media and preventive behaviors: Managing the pandemic through personal responsibility,” Soc. Sci. Med., vol. 277, p. 113928, 2021, doi: 10.1016/j.socscimed.2021.113928.
A. S. M. Alharbi and E. de Doncker, “Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,” Cogn. Syst. Res., vol. 54, pp. 50–61, 2019, doi: 10.1016/j.cogsys.2018.10.001.
H. Li, Q. Chen, Z. Zhong, R. Gong, and G. Han, “E-word of mouth sentiment analysis for user behavior studies,” Inf. Process. Manag., vol. 59, no. 1, p. 102784, 2022, doi: 10.1016/j.ipm.2021.102784.
H. N. Irmanda and Ria Astriratma, “Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 915–922, 2020, doi: 10.29207/resti.v4i5.2313.
C. Liu et al., “Improving sentiment analysis accuracy with emoji embedding,” J. Saf. Sci. Resil., vol. 2, pp. 246–252, 2021, doi: 10.1016/j.jnlssr.2021.10.003.
K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput. J., vol. 97, p. 106754, 2020, doi: 10.1016/j.asoc.2020.106754.
V. García-Díaz, J. P. Espada, R. G. Crespo, B. C. Pelayo G-Bustelo, and J. M. Cueva Lovelle, “An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis,” Appl. Soft Comput. J., vol. 67, pp. 822–833, 2018, doi: 10.1016/j.asoc.2017.05.038.
D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Penerapan Algoritma Svm Untuk Analisis Sentimen Pada Data Twitter Komisi Pemberantasan Korupsi Republik Indonesia,” Edutic - Sci. J. Informatics Educ., vol. 7, no. 1, pp. 1–11, 2020, doi: 10.21107/edutic.v7i1.8779.
D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass Svm Pada Opini Publik Berbahasa Indonesia Di Twitter,” J. Tekno Kompak, vol. 14, no. 2, pp. 86–91, 2020, doi: 10.33365/jtk.v14i2.792.
F. F. Irfani, “Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine,” JBMI (Jurnal Bisnis, Manajemen, dan Inform., vol. 16, no. 3, pp. 258–266, 2020, doi: 10.26487/jbmi.v16i3.8607.
U. Rofiqoh, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter dengan Metode Support Vector Machine dan Lexicon Based Features Twitter event detection View project Human Detection and Tracking View project,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 12, pp. 1725–1732, 2017, [Online]. Available: https://www.researchgate.net/publication/320234928.
R. S. P. Wanda Athira Luqyana, Imam Cholissodin, “Analisis Sentimen Cyberbullying Pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 11, pp. 4704–4713, 2018, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3051.
Abstract viewed = 219 times
PDF downloaded = 172 times
Copyright (c) 2023 Reinhard Alfaries Saemani, Nina Setiyawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
