Sentimen Analisis Inisiatif Vaksin Nasional Menggunakan Naïve Bayes dan Laplacian Smoothing Pada Komentar Video Youtube

Authors

  • I Putu Agus Eka Darma Udayana Institut Bisnis dan Teknologi Indonesia
  • I Gede Iwan Sudipa Institut Bisnis dan Teknologi Indonesia http://orcid.org/0000-0002-4278-9068
  • Risaldi Risaldi Institut Bisnis dan Teknologi Indonesia

DOI:

https://doi.org/10.31598/jurnalresistor.v5i2.1108

Keywords:

Covid 19, Social Media Mining, Naïve Bayes, Sentiment Analysis

Abstract

COVID-19 pandemic that has been declared by who in march 2020 Has been Indonesia biggest health crisis end in the decade. WHO said one of the quickest way to end the pandemic is through immunity through vaccine thu's theory is a national vaccine program initiated by the government in the middle of 2021. YouTube is of de facto public space in Indonesia cyberspace for its netizen for various conversation.  from gossiping to discuss in public policy YouTube has been a gold mine for social media data mining enthusiast since 2010. but has been not utilized much by Indonesia Academic.  do lack of popularity compared to Twitter which has been a media darling what Indonesian Acdemic ever since This research is focused on sentiment analysis pantydeal YouTube about the national vaccine initiation on a news channel in YouTube.  This research is primarily consist  of naive bayes classifier a  a popular algorithm Indonesian data mining enthusiast   which has some limitation such as  the problem known as zero probability problem and also the use of non-public data which  can be fixed  by the use of Laplacian smoothing algorithm  which when tested Using 100 of random comments as a data testing has resulted in 71% percent of succes rate and when we do a statistical analysis the precision , recall rate and the F-meassurement score of the classifier all resulted in above 75% score which is satisfactory.

Downloads

Download data is not yet available.

References

N. G. Bacaksizlar, S. Shaikh, and M. Hadzikadic, “Anger in protest networks on twitter,” Multi Conf. Comput. Sci. Inf. Syst. MCCSIS 2019 - Proc. Int. Conf. ICT, Soc. Hum. Beings 2019, Connect. Smart Cities 2019 Web Based Communities Soc. Media 2019, no. July, pp. 415–419, 2019, doi: 10.33965/wbc2019_201908c054.

A. E. Khedr, S. E. Salama, and N. Yaseen, “Predicting stock market behavior using data mining technique and news sentiment analysis,” Int. J. Intell. Syst. Appl., vol. 9, no. 7, pp. 22–30, 2017, doi: 10.5815/ijisa.2017.07.03.

L. Oikonomou and C. Tjortjis, “A Method for Predicting the Winner of the USA Presidential Elections using Data extracted from Twitter,” in South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA_CECNSM 2018, 2018, no. July. doi: 10.23919/SEEDA-CECNSM.2018.8544919.

R. A. Laksono, K. R. Sungkono, R. Sarno, and C. S. Wahyuni, “Sentiment analysis of restaurant customer reviews on tripadvisor using naïve bayes,” Proc. 2019 Int. Conf. Inf. Commun. Technol. Syst. ICTS 2019, no. July, pp. 49–54, 2019, doi: 10.1109/ICTS.2019.8850982.

R. Mehra, M. K. Bedi, G. Singh, R. Arora, T. Bala, and S. Saxena, “Sentimental analysis using fuzzy and naive bayes,” in Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017, 2018, vol. 2018-Janua, no. Iccmc, pp. 945–950. doi: 10.1109/ICCMC.2017.8282607.

D. Vijayarani, “Liver Disease Prediction using SVM and Naïve Bayes Algorithms,” Int. J. Sci. Eng. Technol. Res., vol. 4, no. 4, pp. 816–820, 2015.

I. Kuzborskij, F. M. Carlucci, and B. Caputo, “When Naïve Bayes Nearest Neighbors Meet Convolutional Neural Networks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2100–2109, 2016, doi: 10.1109/CVPR.2016.231.

A. Moreno-Ortiz, J. Fernández-Cruz, and C. Pérez-Hernández, “Design and evaluation of SentiEcon: A fine-grained economic/financial sentiment lexicon from a corpus of business news,” Lr. 2020 - 12th Int. Conf. Lang. Resour. Eval. Conf. Proc., no. May, pp. 5065–5072, 2020.

M. R, B. A, and S. K, “COVID-19 Outbreak: Tweet based Analysis and Visualization towards the Influence of Coronavirus in the World,” GEDRAG Organ. Rev., vol. 33, no. 02, pp. 534–549, 2020, doi: 10.37896/gor33.02/062.

W. A. Social and Hootsuite, “Digital 2021 : INDONESIA,” Simon Kemp, p. 103, 2021.

I. P. Windasari, F. N. Uzzi, and K. I. Satoto, “Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek,” Proc. - 2017 4th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2017, vol. 2018-Janua, pp. 266–269, 2017, doi: 10.1109/ICITACEE.2017.8257715.

W. Budiharto and M. Meiliana, “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis,” J. Big Data, vol. 5, no. 1, pp. 1–10, 2018, doi: 10.1186/s40537-018-0164-1.

A. M. Barik, R. Mahendra, and M. Adriani, “Normalization of Indonesian-English Code-Mixed Twitter Data,” pp. 417–424, 2019, doi: 10.18653/v1/d19-5554.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

Z. Al-Halah, A. Aitken, W. Shi, and J. Caballero, “Smile, be happy :) emoji embedding for visual sentiment analysis,” Proc. - 2019 Int. Conf. Comput. Vis. Work. ICCVW 2019, pp. 4491–4500, 2019, doi: 10.1109/ICCVW.2019.00550.

V. Cherian and M. S. Bindu, “Heart Disease Prediction Using Naïve Bayes Algorithm and Laplace Smoothing Technique,” Int. J. Comput. Sci. Trends Technol., vol. 5, no. 2, pp. 68–73, 2017.

H. A. Santoso, E. H. Rachmawanto, A. Nugraha, A. A. Nugroho, D. R. I. M. Setiadi, and R. S. Basuki, “Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 2, pp. 799–806, 2020, doi: 10.12928/TELKOMNIKA.V18I2.14744.

I. H. Sarker, M. A. Kabir, A. Colman, and J. Han, “An improved Naive Bayes classifier-based noise detection technique for classifying user phone call behavior,” Commun. Comput. Inf. Sci., vol. 845, no. AusDM 2017, pp. 72–85, 2018, doi: 10.1007/978-981-13-0292-3_5.

Y. Peng, Q. Chen, and Z. Lu, “An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining,” no. 1, pp. 205–214, 2020, doi: 10.18653/v1/2020.bionlp-1.22.

E. I. W. M. X. L. Evmsyw, S. J. Sqtyxiv, R. Rxy, and I. H. Y. Wk, “IWFPE 2012 Poster Presentation,” p. 2012, 2012.

Downloads

Published

2022-10-28

Issue

Vol. 5 No. 2 (2022): Jurnal RESISTOR Edisi Oktober 2022

Section

Articles

License

Copyright (c) 2023 I Putu Agus Eka Darma Udayana, I Gede Iwan Sudipa, Risaldi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright in each article belongs to the author.

  1. The authors admit that RESISTOR Journal as a publisher who published the first time under   Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
  2. Authors can include writing separately, regulate distribution of non-ekskulif of manuscripts that have been published in this journal into another version (eg sent to respository institution author, publication into a book, etc.), by recognizing that the manuscripts have been published for the first time in RESISTOR Journal