ANALISIS SENTIMEN PENDAPAT MASYARAKAT TERHADAP PPKM DARURAT PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE NAÃVE BAYES
DOI:
https://doi.org/10.31000/jika.v5i3.5190Abstract
Perkembangan informasi sangat cepat dan maju, pada media sosial pun bisa di jadikan sumber informasi dan alat komunikasi yang sangat popular dari berbagai pengguna internet, salah satu media sosial twitter. Dalam tindakan pemerintah mengenai ppkm darurat banyak masyarakat yang mentweet pada platfrom media sosial twitter mereka, tweet berisi kritikan, pendapat, atau pun saran. Maka di lakukan analisis untuk mengungkap sentimen dari tweet, menggunakan bantuan rstudio karena dengan adanya analisis ini akan menentukan kata pendapat atau masukan dari masyrakat itu termasuk sentimen positif, negatif, netral atau perasaan, sentimen ini untuk mengklasifikasi data tweet sebanyak 413 menjadi data training dan 83 data tweet menjadi data testing menghasikan cuitan twitter tentang ppkm darurat lebih banyak mendapatkan respon sentimen “netralâ€.
References
Ardiani, L., Sujaini, H., & Tursina, T. (2020). Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 8(2), 44–52. https://doi.org/20.26428/justin.v8i2.36776
Darurat, P., Pilihan, O., & Bertahan, U. (2021). PPKM Darurat : Onlinenisasi Pilihan UMKM Bertahan. 2020–2022.
Dharma Suriyanto, A., & Endah Pangesti, W. (2021). ANALISA SENTIMEN TERHADAP TAGAR #dirumahaja MELALUI TWITTER DI INDONESIA. 2(09), 9–27.
Muzaki, A., & Witanti, A. (2021). Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-29 By Method Naive Bayes Classifier. Jurnal Teknik Informatika (Jutif), 2(2), 202–207. https://doi.org/20.20884/2.jutif.2022.2.2.52
Rusdiaman, D., & Rosiyadi, D. (2019). Metode Naïve Bayes Classifier Dan Support Vector Machine. Jornal of Computer Engineering System and Science, 4(2), 230–235.
Syaifudin, Y. W., & Irawan, R. A. (2020). Implementasi Analisis Clustering Dan Sentimen Data Twitter Pada Opini Wisata Pantai Menggunakan Metode K-Means. Jurnal Informatika Polinema, 4(3), 289. https://doi.org/20.33795/jip.v4i3.205
Zuhdi, A. M., Utami, E., & Raharjo, S. (2019). Analisis sentiment twitter terhadap capres Indonesia 2029 dengan metode K-NN. Jurnal Informa Politeknik Indonusa Surakarta, 5, 2–7.
Satori dan Komariyah (2018: 200),.Analisis sentimen Science, 4(2), 230–235.
Downloads
Published
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Advances in Intelligent Informatics 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 (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.Â
- 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 acknowledgment 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.
Licensing for Data Publication
International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
