Moderna's Vaccine Using the K-Nearest Neighbor (KNN) Method: An Analysis of Community Sentiment on Twitter
DOI:
10.29303/jppipa.v9i5.3203Published:
2023-05-31Issue:
Vol. 9 No. 5 (2023): MayKeywords:
Evaluation Measure, K-Nearest Neighbor, Moderna Vaccines, TF-IDF, TwitterResearch Articles
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Abstract
The COVID-19 is still in Indonesia. The government has made efforts to stop the COVID-19 virus, by moving vaccination program. There are various types of vaccines, one of which is moderna vaccine or MRNA-1273 that applied intramuscularly. The vaccination programs using modern vaccines creates different opinions in public, especially among Twitter users. The opinion uploaded will be the data on Public Sentiment Analysis on Twitter About Moderna Vaccines Using K-Nearest Neighbor Method research. In this study, TF-IDF method is used for weighting the words and KNN for classifying the sentiment into two groups of sentiments, namely positive and negative. The tools used in this research are Rapid miner to collect tweet data and Python for sentiment classification and evaluation. From the test results Based on 50 training data when k = 3 it is known that the accuracy value is 80%, precision is 80%, recall is 100% and F-Measure is 89%.
References
Aher, S. B., & Lobo, L. (2012). Course recommender system in E-learning. International Journal of Computer Science and Communication, 3(1), 159–164. Retrieved from http://csjournals.com/IJCSC/PDF3-1/Article_35.pdf
Akbar, A. S., & Kusumodestoni, R. H. (2020). Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel. Jurnal Teknologi Dan Sistem Komputer, 8(3), 246–254. https://doi.org/10.14710/jtsiskom.2020.14007
Akhtar, A., Akhtar, S., Bakhtawar, B., Kashif, A. A., Aziz, N., & Javeid, M. S. (2021). COVID-19 detection from CBC using machine learning techniques. International Journal of Technology, Innovation and Management (IJTIM), 1(2), 65–78. https://doi.org/10.54489/ijtim.v1i2.22
Alshuwaier, F., Areshey, A., & Poon, J. (2022). Applications and Enhancement of Document-Based Sentiment Analysis in Deep learning Methods: Systematic Literature Review. Intelligent Systems with Applications, 200090. https://doi.org/10.1016/j.iswa.2022.200090
Arslan, H., & Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Engineering Science and Technology, an International Journal, 24(4), 839–847. https://doi.org/10.1016/j.jestch.2020.12.026
Baj, A., Dalla Gasperina, D., Focosi, D., Forlani, G., Ferrante, F. D., Novazzi, F., Azzi, L., & Maggi, F. (2022). Safety and immunogenicity of synchronous COVID19 and influenza vaccination. Journal of Clinical Virology Plus, 2(3), 100082. https://doi.org/10.1016/j.jcvp.2022.100082
Harapan, H., Wagner, A. L., Yufika, A., Winardi, W., Anwar, S., Gan, A. K., Setiawan, A. M., Rajamoorthy, Y., Sofyan, H., & Mudatsir, M. (2020). Acceptance of a COVID-19 vaccine in Southeast Asia: a cross-sectional study in Indonesia. Frontiers in Public Health, 8, 381. https://doi.org/10.3389/fpubh.2020.00381/full
Hofmann, M., & Klinkenberg, R. (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Isnain, A. R., Supriyanto, J., & Kharisma, M. P. (2021). Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121–130. https://doi.org/10.22146/ijccs.65176
Iwendi, C., Mahboob, K., Khalid, Z., Javed, A. R., Rizwan, M., & Ghosh, U. (2021). Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system. Multimedia Systems, 1–15. https://doi.org/10.1007/s00530-021-00774-w
Khalid, M., Ashraf, I., Mehmood, A., Ullah, S., Ahmad, M., & Choi, G. S. (2020). GBSVM: sentiment classification from unstructured reviews using ensemble classifier. Applied Sciences, 10(8), 2788. https://doi.org/10.3390/app10082788
Kunnakorntammanop, S., Thepwuttisathaphon, N., & Thaicharoen, S. (2019). An experience report on building a big data analytics framework using Cloudera CDH and RapidMiner Radoop with a cluster of commodity computers. Soft Computing in Data Science: 5th International Conference, SCDS 2019, Iizuka, Japan, August 28-29, 2019, Proceedings 5, 208–222. https://doi.org/10.1007/978-981-15-0399-3_17
Lestari, S., & Saepudin, S. (2021). Analisis sentimen vaksin sinovac pada twitter menggunakan algoritma Naive Bayes. Seminar Nasional Sistem Informasi Dan Manajemen Informatika Universitas Nusa Putra, 1(01), 163–170. Retrieved from https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/23
Na’iema, A.-N. S., Mulyo, H., & Widiastuti, N. A. (2022). Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor. Jurnal Teknologi Dan Sistem Komputer, 10(1), 32–37. https://doi.org/10.14710/jtsiskom.2022.14110
Naresh, P. K. M., & Kiran, P. (2019). Preprocessing Methods for Unstructured Healthcare Text Dat. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(2S), 715–719. Retrieved from https://www.ijitee.org/wp-content/uploads/papers/v9i2S/B10241292S19.pdf
Nguyen, H., Veluchamy, A., Diop, M., & Iqbal, R. (2018). Comparative study of sentiment analysis with product reviews using machine learning and lexicon-based approaches. SMU Data Science Review, 1(4), 7. Retrieved from https://scholar.smu.edu/datasciencereview/vol1/iss4/7/
Nikmatun, I. A., & Waspada, I. (2019). Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 10(2), 421–432. https://doi.org/10.24176/simet.v10i2.2882
Perez, F., & Granger, B. E. (2015). Project Jupyter: Computational narratives as the engine of collaborative data science. Retrieved September, 11(207), 108. Retrieved from https://blog.jupyter.org/project-jupyter-computational-narratives-as-the-engine-of-collaborative-data-science-2b5fb94c3c58?gi=fee115e4abfe
Ramadhani, S. H., & Wahyudin, M. I. (2022). Analisis Sentimen Terhadap Vaksinasi Astra Zeneca pada Twitter Menggunakan Metode Na{"i}ve Bayes dan K-NN. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(4), 526–534. https://doi.org/10.35870/jtik.v6i4.530
Rizki, M. M., & others. (2019). Analisis sentimen terhadap produk otomotif dari twitter menggunakan kombinasi algoritma k-nearest neighbor dan pendekatan lexicon (studi kasus: mobil toyota). Fakultas Sains dan Teknologi Universitas Islam Negeri Syarif Hidayatullah. Retrieved from https://repository.uinjkt.ac.id/dspace/handle/123456789/48643
Satrio, R. H., & Fauzi, M. A. (2019). Klasifikasi Tweets Pada Twitter Menggunakan Metode K-Nearest Neighbour (K-NN) Dengan Pembobotan TF-IDF. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(8), 8293–8300. Retrieved from https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6133
Shah, K., Patel, H., Sanghvi, D., & Shah, M. (2020). A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augmented Human Research, 5, 1–16. https://doi.org/10.1007/s41133-020-00032-0
Silalahi, A. P., & Simanullang, H. G. (2022). Prediksi Jumlah Pasien Covid-19 Di Indonesia Menggunakan Least Square Method Berbasis Android. Informatika, 14(1), 86–93. https://doi.org/10.36723/juri.v14i1.328
Sudarsono, B. G., Leo, M. I., Santoso, A., & Hendrawan, F. (2021). Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner. JBASE-Journal of Business and Audit Information Systems, 4(1). https://doi.org/10.30813/jbase.v4i1.2729
Veritawati, I., Wasito, I., & Basaruddin, T. (2015). Text preprocessing using annotated suffix tree with matching keyphrase. International Journal of Electrical and Computer Engineering, 5(3), 409. https://doi.org/10.11591/ijece.v5i3.pp409-420
Zuraimi, M. A. Bin, & Zaman, F. H. K. (2021). Vehicle detection and tracking using YOLO and DeepSORT. 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 23–29. https://doi.org/10.1109/ISCAIE51753.2021.9431784
Author Biographies
Marlyna Infryanty Hutapea, Universitas Methodist Indonesia, Jl. Hang Tuah No.8, Madras Hulu, Kec. Medan Polonia, Kota Medan, Sumatera Utara, Indonesia 20151.
Arina Prima Silalahi, Universitas Methodist Indonesia, Jl. Hang Tuah No.8, Madras Hulu, Kec. Medan Polonia, Kota Medan, Sumatera Utara, Indonesia 20151.
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