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Abstract
In October 2022, there were many cases of children suffering from acute kidney failure due to harmful chemical compounds detected in the history of children's cough medicine use. The statement caused controversy and became a conversation on social media, especially Twitter. Exploring this opinion can lead to a decision that can be applied in machine learning and sentiment analysis, namely support vector machines (SVM). The purpose of this research is to find out the sentiment of the community towards drugs that cause acute kidney failure in children and see the performance of the support vector machine algorithm. The data used was 1128. Based on the results of the study, the community responded negatively to this topic, as evidenced by the fact that the negative sentiment obtained was greater than the positive sentiment, and the support vector machine algorithm with a linear kernel performed very well, as evidenced by the excellent accuracy value of 91%.
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References
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References
Alamsyah, M. K., & Pratiwi, N. (2024). Analisis Sentimen Terkait Opini Masyarakat Terhadap Perkembangan E-Sport Mobile Di Indonesia Menggunakan K Nearest Neighbor. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(1), 349–359. https://doi.org/10.29100/jipi.v9i1.4927
CNNIndonesia. (2022). LIVE UPDATE: Obat Sirop Kelebihan EG dan DEG Ditarik dari Peredaran. CNN Indonesia.
DataIndonesia. (2023). Indonesia Masuk Negara Paling Banyak Main Twitter pada Awal 2023. Data Indonesia.
Fitri Wulandari, Elin Haerani, Muhammad Fikry, & Elvia Budianita. (2023). Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier. Jurnal CoSciTech (Computer Science and Information Technology), 4(1), 88–96. https://doi.org/10.37859/coscitech.v4i1.4781
Hasibuan, M. S., & Serdano, A. (2022). Analisis Sentimen Kebijakan Pembelajaran Tatap Muka Menggunakan Support Vector Machine dan Naive Bayes Policy Sentiment Analysis Face-to-face Learning Using Supports Vector and Naive Bayes Engines. 6(2), 199–204.
Kemenkes RI. (2022). Waspadai Gagal Ginjal Akut pada Anak. Kemenkes.Go.Id.
Kevin, V., Que, S., Iriani, A., & Purnomo, H. D. (2020). Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization ( Online Transportation Sentiment Analysis Using Support Vector Machine Based on Particle Swarm Optimization ). 9(2), 162–170.
Lestari, U., Romadhani, T., Suraya, S., & Fatkhiyah, E. (2022). Sentiment Analysis for Extracting Student Opinion Data on Higher Education Services Using the Naive Bayes Classifier and Support Vector Machine Methods (Case Study Akprind Institute of Science and Technology Yogyakarta). Jurnal TAM (Technology Acceptance Model), 13(1), 51. https://doi.org/10.56327/jurnaltam.v13i1.1220
Palepa, M. J., Pratiwi, N., & Rohmansa, R. Q. (2024). Analisis Sentimen Masyarakat Tentang Pengaruh Politik Identitas Pada Pemilu 2024 Terhadap Toleransi Beragama Menggunakan Metode K - Nearest Neighbor. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(1), 389–401. https://doi.org/10.29100/jipi.v9i1.4957
Romadoni, F., Umaidah, Y., & Sari, B. N. (2020). Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 9(2), 247–253. https://doi.org/10.32736/sisfokom.v9i2.903
Sari, B. W., & Haranto, F. F. (2019). Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom Dan Biznet. Jurnal Pilar Nusa Mandiri, 15(2), 171–176. https://doi.org/10.33480/pilar.v15i2.699
Setiawan, H., Utami, E., & Sudarmawan, S. (2021). Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi Dan Informatika), 5(1), 43–51. https://doi.org/10.31603/komtika.v5i1.5189
Shiddicky, A., & Agustian, S. (2022). Jurnal Computer Science and Information Technology ( CoSciTech ) menggunakan metode logistic regression. Jurnal Computer Science and Information Technology (CoSciTech), 3(2), 91–98.
Syah, H., & Witanti, A. (2022). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm). Jurnal Sistem Informasi Dan Informatika (Simika), 5(1), 59–67. https://doi.org/10.47080/simika.v5i1.1411
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