Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification

  • Ahlida Nikmatul H Universitas Muhammadiyah Malang
  • Didih Rizki C Universitas Muhammadiyah Malang
  • Christian S.K. Aditya Universitas Muhammadiyah Malang
Keywords: Cyberbullying, Mobile Legends, Naive Bayes, Gain Rasio

Abstract

One of the social media crimes that is rampant in the current era is cyberbullying. Cyberbullying is a form of intimidation by someone to harass other people using technological devices. this research uses a design for information decision making that aims to get the expected results. the data collection process is carried out manually with a time frame of 1 week by watching the live broadcast of the online game YouTube streamer then sorting out some bullying and non-bullying comments in the comment’s column. Data labeling is done manually. The data obtained amounted to 1000 with 500 negative comments and 500 positive comments. The above test can be concluded that from the distribution of test data there are 90% - 10% have results that are superior to the results of other tests with an increase of 4% in the Naïve Bayes weighting Gain Ratio method. Based on the test data, the results of precision, recall, F1-score and accuracy of the Naïve Bayes classification method are obtained. The test analysis above can be concluded that from the distribution of test data, 90% - 10% have results that are superior to other test results with a 4% increase in the Naïve Bayes weighting Gain Ratio method. The existence of increased accuracy results is due to a randomized data processing process.

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References

C. Zhu, S. Huang, R. Evans, and W. Zhang, “Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures,” Frontiers in Public Health, vol. 9. 2021. doi: 10.3389/fpubh.2021.634909.

M. J. Wang, K. Yogeeswaran, N. P. Andrews, D. R. Hawi, and C. G. Sibley, “How Common Is Cyberbullying among Adults? Exploring Gender, Ethnic, and Age Differences in the Prevalence of Cyberbullying,” Cyberpsychol Behav Soc Netw, vol. 22, no. 11, 2019, doi: 10.1089/cyber.2019.0146.

P. Strickland and J. Dent, “Online harassment and cyber bullying,” House of Commons, no. 07967, 2017.

M. Weinstein, M. R. Jensen, and B. M. Tynes, “Victimized in many ways: Online and offline bullying/harassment and perceived racial discrimination in diverse racial–ethnic minority adolescents.,” Cultur Divers Ethnic Minor Psychol, vol. 27, no. 3, 2021, doi: 10.1037/cdp0000436.

Samsir et al., “Naives Bayes Algorithm for Twitter Sentiment Analysis,” J Phys Conf Ser, vol. 1933, no. 1, p. 012019, Jun. 2021, doi: 10.1088/1742-6596/1933/1/012019.

D. Irmayani, F. Edi, J. M. Harahap, and ..., “Naives Bayes Algorithm for Twitter Sentiment Analysis,” Journal of Physics …, 2021, [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1933/1/012019/meta

C. A. P. Dita, P. Chairunisyah, and M. Mesran, “Penerapan Naive Bayesian Classifier Dalam Penyeleksian Beasiswa PPA,” Journal of Computer System and Informatics (JoSYC), vol. 2, no. 2, pp. 194–198, 2021.

Y. Findawati, I. R. I. Astutik, A. S. Fitroni, I. Indrawati, and N. Yuniasih, “Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast,” J Phys Conf Ser, vol. 1402, p. 066046, Dec. 2019, doi: 10.1088/1742-6596/1402/6/066046.

K. U. Santoshi, S. S. Bhavya, Y. B. Sri, and B. Venkateswarlu, “Twitter Spam Detection Using Naïve Bayes Classifier,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, Jan. 2021, pp. 773–777. doi: 10.1109/ICICT50816.2021.9358579.

C. Zhang, G.-R. Xue, Y. Yu, and H. Zha, “Web-scale classification with naive bayes,” Proceedings of the 18th international conference on World wide web - WWW ’09, p. 1083, 2009, doi: 10.1145/1526709.1526867.

Published
2023-03-31
How to Cite
Ahlida Nikmatul H, Didih Rizki C, & Christian S.K. Aditya. (2023). Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification. Journal of Systems Engineering and Information Technology (JOSEIT), 2(1), 25-28. https://doi.org/10.29207/joseit.v2i1.5016
Section
Articles