SENTIMENT ANALYSIS ABOUT INDONESIAN PEOPLE'S AWARENESS ABOUT CYBER SECURITY IN DETERMINING DATA LEAKAGE USING NAÏVE BAYES CLASSIFIER ALGORITHM
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Jul 25, 2023
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Abstract
Leakage of personal data is a serious challenge, the adverse effects of incidents of data leakage can result in fines, loss of reputation and public trust that has been harmed. Cyber security plays a role in minimizing the level of risk of cyberattack threats that disrupt the security of all system components such as infrastructure, software, hardware and data. This research was conducted to analyze the sentiments of the Indonesian people towards the queries "cyber security" and "data leaks" from Twitter user tweets. The data classification in this study uses the Naïve Bayes Classifier algorithm method. The purpose of this study is to determine the percentage performance of positive and negative sentiments. The results of the modeling performance are obtaining accuracy percentage values of 88.80%, Precision 29.27%, and Recall 30.77%. The results of the data visualization show that Indonesian people, especially Twitter users, give many negative opinions in response to this discussion. So the percentage of negative sentiment is higher than positive sentiment.
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Nurfadlillah, D. (2023). SENTIMENT ANALYSIS ABOUT INDONESIAN PEOPLE’S AWARENESS ABOUT CYBER SECURITY IN DETERMINING DATA LEAKAGE USING NAÏVE BAYES CLASSIFIER ALGORITHM. Electro Luceat, 9(1), 64 - 72. https://doi.org/10.32531/jelekn.v9i1.598
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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[4] N. I. Wibowo, T. A. Maulana, H. Muhammad, and N. A. Rakhmawati, “Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 2, pp. 120–129, 2021, doi:
10.14421/jiska.2021.6.2.120-129.
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Bayes,” vol. 1, no. 2, pp. 120–126, 2015.
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Naïve Bayes , K-Nearest Neighboars Dan Decision Tree,” vol. 6, no. April, pp. 746–750, 2022, doi: 10.30865/mib.v6i2.3554.
[14] F. Yahyadi, Adi; Latifah, “Analisis Sentimen Twitter Terhadap Kebijakan Ppkm Di Tengah
Pandemi Covid-19,” J. Inf. Syst. Applied, Manag. Account. Res., vol. 6, no. 2, pp. 464–471, 2022, doi: 10.52362/jisamar.v6i2.791.
[15] T. Wiratama Putra, A. Triayudi, and A. Andrianingsih, “Analisis Sentimen Pembelajaran Daring Menggunakan Metode Naïve Bayes, KNN, dan Decision Tree,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 6, no. 1, pp. 20–26, 2022, doi: 10.35870/jtik.v6i1.368.
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[17] A. Muhson, “Teknik Analisis Kuantitatif 1 TEKNIK ANALISIS KUANTITATIF,” 2006.
[18] A. Dewandaru and J. S. Wibowo, “Jurnal Teknologi dan Manajemen Informatika Analisis Sentimen dan Klasifikasi Tweet Terkait Mutasi COVID-19 Menggunakan Metode Naïve Bayes Classifier,” vol. 8, no. 1, pp. 32–38, 2022.
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