Analisis Sentimen Kebijakan Pembelajaran Tatap Muka Menggunakan Support Vector Machine dan Naive Bayes
DOI:
https://doi.org/10.30595/jrst.v6i2.15145Keywords:
Analisis Sentimen, Twitter, TF-IDF, SVM, NBAbstract
Analisis sentimen kebijakan pembelajaran tatap muka selama pandemi dapat dianalisis menggunakan data dari tweet di platform media Twitter. Dari hasil crawling data diperoleh 152 data, selanjutnya dilakukan proses pre-processing untuk memberikan data dari simbol, dan link serta mengubah kata menjadi bobot kata yang dapat dipahami dengan menggunakan teknik TF-IDF. Hasil pembobotan kata ini dianalisis pada kelas positif dan negatif, label positif terkumpul 96 dan negatif 16. Polaritas kelas hasil dari 108 data yang diketahui masyarakat memberikan respon positif terhadap kebijakan pemerintah untuk melakukan pembelajaran tatap muka. Pengujian model Support Vector Machine (SVM) dan Naive Bayes (NB) dari 108 data diperoleh hasil akurasi SVM sebesar 88,09% dan NB 75,92% pada penelitian ini SVM lebih baik dari NB.
References
Amidror, Y. (2018). The evolution and development of the IDF. In Routledge Handbook on Israeli Security. https://doi.org/10.4324/9781315165196-4
Dios, M. T. C., & Charlo, J. C. P. (2021). Face-to-face vs. E-learning models in the covid-19 era: Survey research in a Spanish university. Education Sciences, 11(6). https://doi.org/10.3390/educsci11060293
Giunti, G., Claes, M., Zubiete, E. D., Rivera-Romero, O., & Gabarron, E. (2021). Impact of COVID-19 on multiple sclerosis topic discussion on Twitter. In Public Health and Informatics: Proceedings of MIE 2021. https://doi.org/10.3233/SHTI210302
Jung, Y. (2018). Multiple predicting K-fold cross-validation for model selection. Journal of Nonparametric Statistics, 30(1). https://doi.org/10.1080/10485252.2017.1404598
Kurniawan, r., & apriliani, a. (2020). Analisis sentimen masyarakat terhadap virus corona berdasarkan opini dari twitter berbasis web scraper. Jurnal INSTEK (Informatika Sains Dan Teknologi). https://doi.org/10.24252/instek.v5i1.13686
Maynard, D., Bontcheva, K., & Augenstein, I. (2017). Natural Language Processing for the Semantic Web. In Synthesis Lectures on the Semantic Web: Theory and Technology (Vol. 6, Issue 2). https://doi.org/10.2200/S00741ED1V01Y201611WBE015
MyslÃn, M., Zhu, S. H., Chapman, W., & Conway, M. (2013). Using twitter to examine smoking behavior and perceptions of emerging tobacco products. Journal of Medical Internet Research. https://doi.org/10.2196/jmir.2534
Pilehvar, M. T., & Camacho-Collados, J. (2020). Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. In Synthesis Lectures on Human Language Technologies (Vol. 13, Issue 4). https://doi.org/10.2200/S01057ED1V01Y202009HLT047
Samsir, Ambiyar, Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. Jurnal Media Informatika Budidarma.
Singh, S., & Mahmood, A. (2021). The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2021.3077350
Soufyane, A., Abdelhakim, B. A., & Ahmed, M. Ben. (2021). An intelligent chatbot using NLP and TF-IDF algorithm for text understanding applied to the medical field. In Advances in Science, Technology and Innovation. https://doi.org/10.1007/978-3-030-53440-0_1
Syah, R. H. (2020). Dampak Covid-19 pada Pendidikan di Indonesia: Sekolah, Keterampilan, dan Proses Pembelajaran. SALAM: Jurnal Sosial Dan Budaya Syar-I, 7(5). https://doi.org/10.15408/sjsbs.v7i5.15314
Watrianthos, R. (2020). Analisis Pembelajaran Daring di Era Pandemic Covid-19. Merdeka Kreatif Di Era Pandemi Covid-19.
Wong, T. T., & Yeh, P. Y. (2020). Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Transactions on Knowledge and Data Engineering, 32(8). https://doi.org/10.1109/TKDE.2019.2912815
Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011). Combining lexicon-based and learning-based methods for twitter sentiment analysis. HP Laboratories Technical Report.
Zhou, M., Duan, N., Liu, S., & Shum, H. Y. (2020). Progress in Neural NLP: Modeling, Learning, and Reasoning. In Engineering (Vol. 6, Issue 3). https://doi.org/10.1016/j.eng.2019.12.014
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Muhammad Siddik Hasibuan, Akbar Serdano
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)
JRST (Jurnal Riset Sains dan Teknologi) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
