Text Classification for Analysing Indonesian People's Opinion Sentiment for Covid-19 Vaccination
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A. Susilo, C. M. Rumende, C. W. Pitoyo, W. D. Santoso, M. Yulianti, H. Herikurniawan, R. Sinto, G. Singh, L. Nainggolan, E. J. Nelwan, L. K. Chen, A. Widhani, E. Wijaya, B. Wicaksana, M. Maksum, F. Annisa, C. O. M. Jasirwan, E. Yunihastuti, E. “Coronavirus Disease 2019: Tinjauan Literatur Terkini”. Jurnal Penyakit Dalam Indonesia, vol. 7, no. 1, pp. 45-67, 2020.
Litbangkes. “Tantangan Pelaksanaan Vaksinasi COVID-19 di Indonesia”, Badan Penelitian dan Pengembangan Kesehatan, 2021. [Online}. Available: https://www.litbang.kemkes.go.id/tantangan-pelaksanaan-vaksinasi-covid-19-di-indonesia/
F. Rachman, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter”, Indonesian of Health Information Management Journal (INOHIM), vol. 8, no. 2, pp. 2655–9129, 2020.
K. Perdana, T. Pricillia, “Optimasi TextBlob Menggunakan Support Vector Machine untuk Analisis Sentimen (Studi Kasus Layanan Telkomsel)”, Bangkit Indonesia, vol. X, no. 01, pp. 13–15, 2021.
L. Zhang, B. Liu, “Sentiment Analysis and Opinion Mining”, Encyclopedia of Machine Learning and Data Mining, pp. 1152–1161, 2017.
A. Prasanti, M. A. Fauzi, M. T. Furqon, “Klasifikasi Teks Pengaduan Pada Sambat Online Menggunakan Metode N- Gram dan Neighbor Weighted K-Nearest Neighbor (NW-KNN)” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, vol. 2, no. 2), pp. 594–601, 2018.
E. Fitri, Y. Yuliani, S. Rosyida, W. Gata, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine”, Jurnal Transformatika, vol. 18, issue. 1, pp.. 71-80, 2020.
A. V. Sudiantoro, E. Zuliarso, “Analisis Sentimen Twitter Menggunakan Text Mining Dengan Algoritma Naive Bayes Classifier”. In Prosiding Seminar Nasiional Teknologi Informasi dan Aplikasi Komputer, 2018, pp. 398–401.
C. M. Annur, “Meragukan Efektivitas, Alasan Utama Masyarakat Enggan Vaksin Covid-19 Dosis Kedua”. Katadata Insight Center, 2021 [Online]. Available: https://databoks.katadata.co.id/datapublish/2021/09/22/meragukan-efektivitas-alasan-utama-masyarakat-enggan-vaksin-covid-19-dosis-kedua
S. Z. Wenno, “Pendekatan Random Forest Pada Pohon Klasifikasi Dan Multivariate Adaptive Regression Spline Untuk Keakuratan Klasifikasi Pengguna Narkoba Di Jawa Timur”, Thesis Sarjana Universitas Airlangga, 2017.
F. N. A. Al Omran, C. Treude, “Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments, in Proceedings IEEE/ACM 14th International Conference on Mining Software Repositories (MSR), 2017, pp. 187–197.
N. Hartanto, A. A. B. Raharjo, A. G. Pambudhi, “Implementasi Text Mining Pada Analisis Sentimen Opini Masyarakat Terhadap Hubungan Perdagangan Indonesia Dan China Dengan Teknik Text Classification Naive Bayes”, Tesis Sarjana Universitas Bina Nusantara, 2021.
Gholamy, V. Kreinovich, O. Kosheleva, “Why 70/30 or 80/20 Relation Between Training and Testing Sets : A Pedagogical Explanation”, Departmental Technical Reports (CS), pp. 1–6, 2018.
M. R. Adrian, M. P. Putra, M. H. Rafialdy, N. A. Rakhmawati, N. A, “Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB”, Jurnal Informatika Upgris, vol. 7, issue. 1, pp. 36–40, 2021.
D. Hazarika, G. Konwar, S.Deb, D. J. Bora, “Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing”, in Proceedings of the International Conference on Research in Management & Technovation 2020, 24, pp. 63–67.
A. Fatoni, “Optimasi Aplikasi Antrian Pasien Online Menggunakan Algoritma Patient Treatment Time Prediction”, Thesis Sarjana, Universitas 17 Agustus, 2020.
K. Shah, H. Patel, D. Sanghvi, M. Shah, “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification”,. Augmented Human Research, vol. 5, no.1, pp. 1- 12, 2020.
DOI: https://doi.org/10.32520/stmsi.v12i2.2759
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