Analisis Sentimen Tweet KRI Nanggala 402 di Twitter menggunakan Metode Naïve Bayes Classifier
Article Sidebar
Main Article Content
Abstract
Social media is one of the technological developments that has contributed greatly in making it easy for us to communicate and socialize, one of which is using Twitter social media. Twitter in this study is used as a data source to analyze tweets discussing KRI Nanggala 402. Analysis of KRI Nanggala 402 twitter sentiment is used to see the tendency of public responses to the sinking of the KRI Nanggala 402 submarine whether to give positive or negative opinions. This Sentiment analysis uses the Naïve Bayes Classifier method, which is a classification method. The first research stage is crawling, processing, classification, and evaluation. The classification stage is carried out after the processing phase, where the classification results tend to be positive or negative, using the Naïve Bayes Classifier method. The accuracy of the system in the Sentiment analysis of the KRI Nanggala 402 tweet is 73.00%.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTIK journal and Research Division, KITA Institute as the publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
JTIK journal and Research Division, KITA Institute and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTIK journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTIK]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Muhammad Wali (Editor-in-Chief)
Editorial Office of Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Research Division, KITA Institute
Teuku Nyak Arief Street Nomor : 7b, Lamnyong, Lamgugop, Kota Banda Aceh
Telp./Fax: 0651-8070141
Email: jtik@lembagakita.org - journal@lembagakita.org
References
Ansari, M.Z., Aziz, M.B., Siddiqui, M.O., Mehra, H. and Singh, K.P., 2020. Analysis of political sentiment orientations on twitter. Procedia Computer Science, 167, pp.1821-1828.
Astari, N.M.A.J., Divayana, D.G.H. and Indrawan, G., 2020. Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier. Jurnal Sistem Dan Informatika (JSI), 15(1), pp.27-29.
Syarifuddinn, M., 2020. Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes dan Knn. Inti Nusa Mandiri, 15(1), pp.23-28.
Sari, D.I., Wati, Y.F. and Widiastuti, W.W., 2020. Analisis Sentimen Dan Klasifikasi Tweets Berbahasa Indonesia Terhadap Transportasi Umum Mrt Jakarta Menggunakan Naïve Bayes Classifier. Jurnal Ilmiah Informatika Komputer, 25(1), pp.64-75.
Hadna, N.M.S., Santosa, P.I. and Winarno, W.W., 2016. Studi literatur tentang perbandingan metode untuk proses analisis sentimen di Twitter. Semin. Nas. Teknol. Inf. dan Komun, 2016, pp.57-64.
P. Jurnal and R. T. Informatika, 2020. Algoritma Naïve Bayes Untuk Analisis Sentimen Ulasan Aplikasi Windows Phone Store, vol. 3, no. April 2019, pp. 1–13.
Buntoro, G.A., 2017. Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. INTEGER: Journal of Information Technology, 2(1).
Liu, B., 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), pp.1-167.
Vinodhini, G. and Chandrasekaran, R.M., 2012. Sentiment analysis and opinion mining: a survey. International Journal, 2(6), pp.282-292.
Cambria, E., Schuller, B., Xia, Y. and Havasi, C., 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), pp.15-21.
Ravi, K. and Ravi, V., 2015. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, pp.14-46.
El Husna, R., 2020. Analisis Klasifikasi Sentimen Pada Twitter Mengenai Netflix Yang Diblokir Oleh Telkom Menggunakan Naïve Bayes Classifier Dan Support Vector Machine (Doctoral dissertation, Muhammadiyah University, Semarang).
Rohmah, N.N.M., 2020. Media Sosial sebagai Media Alternatif Manfaat dan Pemuas Kebutuhan Informasi Masa Pandemik Global Covid 19 (Kajian Analisis Teori Uses and Gratification). Al-I'lam: Jurnal Komunikasi dan Penyiaran Islam, 4(1), pp.1-16.
Faradhillah, N.Y., Kusumawardani, R.P. and Hafidz, I., 2016. Eksperimen Sistem Klasifikasi Analisa Sentimen Twitter pada Akun Resmi Pemerintah Kota Surabaya Berbasis Pembelajaran Mesin. SESINDO 2016, 2016.
Wagh, B., 2016. SJV, and WNR,“. Sentimental Analysis on Twitter Data using Naive Bayes,†IJARCCE, 5(12), pp.316-319.