Analisis Sentimen Masyarakat Terhadap Pembatasan Sosial Berksala Besar Menggunakan Algoritma Support Vector Machine
Abstract
The spread of COVID-19 has become a pandemic throughout the world, including Indonesia. As the main door in Maluku, Ambon has become a new epicenter of the spread of coronavirus. This condition requires local governments to implement Large Scale Social Restrictions (LSSR) after being permitted by the Ministry of Health. This has become a polemic for some Ambonese people especially those who don't have a steady income. Many posts from social media discussing the LSSR that was implemented in Ambon. This study aims to find out how people's sentiment towards the LSSR implementation plan in Ambon through tweets and comments on social media platforms using sentiment analysis. The data obtained amounted to 1075 tweets and comments and separated between 350 training data and testing data of 725. The data classified using the Support Vector Machine (SVM) algorithm. The results of the study showed positive sentiment of 28%, a negative sentiment of 27%, and a neutral sentiment of 45%. It can be concluded that public sentiment towards the implementation of LSSR in Ambon based on posts on social media platforms is quite balanced between positive and negative sentiments and also dominated by neutral sentiments posts.
References
Amolik, A., Jivane, N., Bhandari, M., & Venkatesan, M. (2016). Twitter sentiment analysis of movie reviews using machine learning technique. International Journal of Engineering and Technology, 7(6), 2038–2044.
APJII. (2019). Penetrasi & Profil Perilaku Pengguna Internet Indonesia Tahun 2018. Apjii, 51. www.apjii.or.id
Bhonde, S. B., & Prasad, J. R. (2015). Sentiment Analysis - Methods, Applications and Challenges. International Journal of Electronics Communication and Computer Engineering, 6(6Online), 2249–2271.
Bradley, A., & James, R. J. E. (2019). Web Scraping Using R. Advances in Methods and Practices in Psychological Science, 2(3), 264–270. https://doi.org/10.1177/2515245919859535
Denny, M. J., & Spirling, A. (2018). Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, and What to Do about It. Political Analysis, 26(2), 168–189. https://doi.org/10.1017/pan.2017.44
Fernández-Gavilanes, M., ÃÂlvarez-López, T., Juncal-MartÃÂnez, J., Costa-Montenegro, E., & Javier González-Castaño, F. (2016). Unsupervised method for sentiment analysis in online texts. Expert Systems with Applications, 58, 57–75. https://doi.org/10.1016/j.eswa.2016.03.031
Franz, D., Marsh, H. E., Chen, J. I., & Teo, A. R. (2019). Using facebook for qualitative research: A brief primer. Journal of Medical Internet Research, 21(8), 1–12. https://doi.org/10.2196/13544
GmbH, R. (2018). RapidMiner 8 Operator Reference Manual. RapidMiner GmbH. www.rapidminer.com
Gustu COVID-19. (2020). Homepage Gugus Tugas Percepatan Penanganan COVID-19. https://covid19.go.id/
Gustu Covid-19 Kota Ambon. (2020). Situasi Terkini COVID-19 di Kota Ambon. http://www.ambon.go.id/covid-19/
Gustu Covid-19 Maluku. (2020). Homepage COVID-19 Pemprov Maluku. https://corona.malukuprov.go.id/
Kemp, S. (2018). Digital in 2018 in Southeast Asia. We Are Social, 362. https://www.slideshare.net/wearesocial/digital-in-2018-in-southeast-asia-part-2-southeast-86866464%0Ahttps://www.slideshare.net/wearesocial/digital-in-2018-in-southeast-asia-part-1-northwest-86866386%0Ahttps://www.slideshare.net/wearesocial/digital-in-201
Mayaut, P. F. (2020). Wali Kota: PKM masa uji coba penerapan PSBB di Ambon. https://ambon.antaranews.com/berita/81184/wali-kota-pkm-masa-uji-coba-penerapan-psbb-di-ambon
Moreno-Torres, J. G., Saez, J. A., & Herrera, F. (2012). Study on the impact of partition-induced dataset shift on k-fold cross-validation. IEEE Transactions on Neural Networks and Learning Systems, 23(8), 1304–1312. https://doi.org/10.1109/TNNLS.2012.2199516
Patty, R. R. (2020). PSBB Ambon Dimulai 22 Juni, Wali Kota: Pelanggar Diberi Sanksi Tegas. https://regional.kompas.com/read/2020/06/17/17342771/psbb-ambon-dimulai-22-juni-wali-kota-pelanggar-diberi-sanksi-tegas
Purnamasari, D. M. (2020, April 23). Survei: Sebagian Besar Warga Menolak PSBB karena Sulit Cari Nafkah. https://nasional.kompas.com/read/2020/04/23/13541671/survei-sebagian-besar-warga-menolak-psbb-karena-sulit-cari-nafkah
Sauban, A. (2020, April 1). Empat Sektor Ekonomi yang Paling Tertekan Pandemi Covid-19. https://republika.co.id/berita/q83llp409/empat-sektor-ekonomi-yang-paling-tertekan-pandemi-covid19
Setatama, M. S., & Tricahyono, D. (2017). Implementasi Social Network Analysis dalam Penyebaran Country Branding “Wonderful Indonesia.†Ind. Journal on Computing, 2(2), 91–104. https://doi.org/10.1300/J079v16n01_10
Tuhuteru, H., & Iriani, A. (2018). Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier. Jurnal Informatika: Jurnal Pengembangan IT, 3(3), 394–401. https://doi.org/10.30591/jpit.v3i3.977
Vol, E. R., States, U., City, N. Y., Angeles, L., Czeisler, M. É., Tynan, M. A., Howard, M. E., Honeycutt, S., Fulmer, E. B., Kidder, D. P., Robbins, R., Barger, L. K., Facer-childs, E. R., Baldwin, G., Rajaratnam, S. M. W., & Czeisler, C. A. (2020). Public Attitudes , Behaviors , and Beliefs Related to COVID-19 , Stay-at-Home Orders , Nonessential Business Closures , and Public Health Guidance â€â€. 69.
Windasari, I. P., Uzzi, F. N., & Satoto, K. I. (2017). Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. Proceedings - 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2017, 2018-Janua, 266–269. https://doi.org/10.1109/ICITACEE.2017.8257715
Zulfa, I., & Winarko, E. (2017). Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(2), 187. https://doi.org/10.22146/ijccs.24716
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