ANALISIS SENTIMEN TERHADAP WARGA CHINA SAAT PANDEMI DENGANALGORITMATERM FREQUENCY-INVERSE DOCUMENT FREQUENCY DAN SUPPORT VECTOR MACHINE

ANALISIS SENTIMEN TERHADAP WARGA CHINA SAAT PANDEMI DENGANALGORITMATERM FREQUENCY-INVERSE DOCUMENT FREQUENCY DAN SUPPORT VECTOR MACHINE

• By Agustono,Efid, Dwi; Sianturi,Daniel; Taufik,Andi; Gata,Windu;
• Jurnal Nasional Terakreditasi
• 311
• 2620-6897

Sejak merebaknya virus Covid-19 secara global terjadi aksi anti China di berbagai negara. Tingkat kematian atas virus Covid-19 yang cukup tinggi menyebabkan banyak negara mengambil langkah pencegahan yang membatasi aktivitas setiap individu. Di Indonesia virus tersebut sudah menjangkit 34 provinsi dan 415 kabupaten/kota. Berdasarkan penelitian dari Wearesosial Hootsuite yang dipublikasikan pada Januari 2019 jumlah pengguna media sosial di Indonesia mencapai 150 juta pengguna atau mencapai 56 persen jumlah penduduk Indonesia. Twittermerupakan salah satu media sosial populer di mana pengguna dapat membuat status atau disebut "tweets". Kicauan tersebut mengandung banyak ekspresi suka, tidak suka, dan kontribusinya pada berbagai topik. Penelitian ini bertujuan untuk mengetahui sentimen warga Indonesia terhadap warga china yang ada di Indonesia dengan permodelan Term Frequuency-Inverse Document Frequency dan Algoritma Support Vector Machine pada media sosial twitter.

• Katakunci covid-19, sentimen, korona, warga china, pandemi.
• URL http://e-journal.stmiklombok.ac.id/index.php/jire/article/view/258
• Penerbit ,

REFERENSI

[1] W. H. Organization, “WHO Timeline - COVID-19,” 2020, 2020. https://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19 (accessed Apr. 27, 2020).
[2] “Infografis COVID-19 (10 Mei 2020),” 2020, 2020. https://covid19.go.id/p/berita/infografis-covid-19-10-mei-2020 (accessed May 10, 2020).
[3] “COVID-19 CORONAVIRUS PANDEMIC,” 2020, 2020. https://www.worldometers.info/coronavirus/ (accessed Apr. 27, 2020).
[4] Peraturan Pemerintah (PP) No. 21/2020, Pembatasan Sosial Berskala Besar (PSBB) Dalam Rangka Percepatan Penanganan COVID-19. 2020.
[5] “Berapa Pengguna Media Sosial Indonesia?,” 2019, 2019. https://databoks.katadata.co.id/datapublish/2019/02/08/berapa-pengguna-media-sosial-indonesia (accessed May 04, 2020).
[6] M. Kamyab, R. Tao, M. H. Mohammadi, and A. Rasool, “Sentiment analysis on Twitter: A text mining approach to the Afghanistan status reviews,” ACM Int. Conf. Proceeding Ser., pp. 14–19, 2018, doi: 10.1145/3293663.3293687.
[7] S. Rosenthal, A. Ritter, P. Nakov, and V. Stoyanov, “SemEval-2014 Task 9: Sentiment Analysis in Twitter,” 2015, doi: 10.3115/v1/s14-2009.
[8] A. Shelar and C. Y. Huang, “Sentiment analysis of twitter data,” Proc. - 2018 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2018, pp. 1301–1302, 2018, doi: 10.1109/CSCI46756.2018.00252.
[9] M. Lowen, “Covid 19 dan sentimen terhadap orang Asia di Amerika, mereka ‘diludahi, dipukul dan dikata-katai’ selama pandemi,” 2020. https://www.bbc.com/indonesia/majalah-52830126 (accessed Jun. 28, 2020).
[10] M. Lowen, “Virus corona: Italia ‘dihantui’ virus corona, turis dan warga China menjadi sasaran sentimen rasis,” 5 Feb 2020, 2020. https://www.bbc.com/indonesia/majalah-51383085 (accessed Jun. 28, 2020).
[11] Purnama Ayu Rizky, “Sentimen Anti-China Lebih Seram daripada COVID-19 di Indonesia,” 2020. https://www.matamatapolitik.com/sentimen-anti-china-lebih-seram-daripada-covid-19-di-indonesia-in-depth/ (accessed Aug. 28, 2020).
[12] A. Giachanou and F. Crestani, “Like it or not: A survey of Twitter sentiment analysis methods,” ACM Comput. Surv., vol. 49, no. 2, 2016, doi: 10.1145/2938640.
[13] T. K. Das, D. P. Acharjya, and M. R. Patra, “Opinion mining about a product by analyzing public tweets in Twitter,” in 2014 International Conference on Computer Communication and Informatics: Ushering in Technologies of Tomorrow, Today, ICCCI 2014, 2014, doi: 10.1109/ICCCI.2014.6921727.
[14] W. Gata, “Akurasi Text Mining Menggunakan Algoritma K-Nearest Neighbour pada Data Content Berita SMS,” vol. 6, pp. 1–13, 2017.
[15] Frank Chung, “‘Asian dog, you brought corona here’: Young women racially abused, spat on in Sydney street,” 2020, [Online]. Available: https://www.news.com.au/national/nsw-act/crime/asian-dog-you-brought-corona-here-young-women-racially-abused-spat-on-in-sydney-street/news-story/628e1cbadf94bfdd30fe6dfd4ebe81e8.
[16] T. H. E. Reaction and O. F. Isis, “IPAC Short Briefing No . 1 : COVID-19 AND ISIS IN INDONESIA,” no. 1, 2020.
[17] I. Santoso, W. Gata, and A. B. Paryanti, “JURNAL RESTI Penggunaan Feature Selection di Algoritma Support Vector Machine untuk,” vol. 1, no. 10, pp. 5–11, 2019.
[18] B. Pratama et al., “Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM and NB Methods,” J. Phys. Conf. Ser., vol. 1201, no. 1, 2019, doi: 10.1088/1742-6596/1201/1/012038.
[19] S. Kurniawan, W. Gata, D. A. Puspitawati, N. -, M. Tabrani, and K. Novel, “Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 176–183, 2019, doi: 10.29207/resti.v3i2.935.
[20] A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. March, pp. 1704–1708, 2018.
[21] T. Evgeniou and M. Pontil, “Workshop on Support Vector Machines : Theory and Applications,” Mach. Learn. Its Appl. Adv. Lect., no. January 2001, pp. 249–257, 2001, doi: 10.1007/3-540-44673-7.
[22] F. Hermens, “Book Reviews: Book Reviews,” Rev. Soc. Econ., vol. 40, no. 1, pp. 72–74, 1982, doi: 10.1080/00346768200000022.
[23] D. Mining and T. Mining, “A Programmers Guide to Data Mining,” Text, 2017, doi: 10.1109/ISPLC.2012.6201332.
[24] R. Melita et al., “( Tf-Idf ) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web ( Studi Kasus : Syarah Umdatil Ahkam ),” vol. 11, no. 2, 2018.
[25] V. S and J. R, “Text Mining: open Source Tokenization Tools – An Analysis,” Adv. Comput. Intell. An Int. J., 2016, doi: 10.5121/acii.2016.3104.
[26] A. Smote and D. A. N. Neighbor, “Klasifikasi Data Tidak Seimbang Menggunakan,” vol. 3, no. 1, pp. 44–49.
[27] Lombok, K. I. S. (2015). Perencanaan Strategi Sistem Informasi Pendidikan Pada Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Lombok. Bianglala Informatika, 3(2).