Authors:

Fathiyarizq Mahendra Putra, I Wayan Santiyasa

Abstract:

“Corona Virus Disease or COVID 19 is a new virus disease that originated in 2019 [6], Indonesia has reported first COVID-19 In 2nd March 2020. Various attempts have been made by the government, such as taking strict measures by temporal lockdown or cordoning off the areas that were suspected of having risks of community spread. As a source of information, the internet has changed substantially,. for example, social media. social media is a communication tool that is very popular among internet users today, From social media, users can update status, send messages, even, become a platform for exchanging socio-economic opinions and political views both in their place of residence or their country. This paper deals with the sentiment analysis of Indonesian after the peformance of Indonesian Ministry Of Health. We used the social media platform Twitter for our analysis. Tweets were studied to gauge the opinion of Indonesian towards peformance of Indonesian Ministry Of Health. Tweets were extracted using the two prominent keywords used namely: “terawan ”and “menkes” from June 15th to September 19th 2020. A total of 200 tweets were considered for the analysis. This study has successfully implemented the SVM algorithm for sentiment analysis on tweet data about peformance of Indonesian Ministry Of Health during COVID-19 Crisis. This is shown by the accuracy of using tweet data as much as 200 data, which is 172 data are training data and 28 are testing data. Besides the amount of data that affects accuracy, there are also other factors, namely the use of the kernel and the number of classes used. The results show that the Linear Kernel has the best accuracy, precision and recall rate compared to other kernels, respectively 75% for accuracy, 78.4% for precision and a recall value of 75%. for polynomial kernels, Gaussian and Sigmoid have the same accuracy, precision, and recall rates, namely, respectively. 60.71% for accuracy, 36.86% for precision and 60.71% recall value.”

Keywords

used namely: “terawan ”and “menkes” from June 15th to September 19th 2020. A total of 200 tweets were considered for the analysis. This study has successfully implemented the SVM algorithm for sentiment analysis on tweet data about peformance of Indonesian Ministry Of Health during COVID-19 Crisis. This is shown by the accuracy of using tweet data as much as 200 data, which is 172 data are training data and 28 are testing data. Besides the amount of data that affects accuracy, there are also other factors, namely the use of the kernel and the number of classes used. The results show that the Linear Kernel has the best accuracy, precision and recall rate compared to other kernels, respectively 75% for accuracy, 78.4% for precision and a recall value of 75%. for polynomial kernels, Gaussian and Sigmoid have the same accuracy, precision, and recall rates, namely, respectively. 60.71% for accuracy, 36.86% for precision and 60.71% recall value.

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PDF:

https://jurnal.harianregional.com/jlk/full-64433

Published

2021-08-06

How To Cite

PUTRA, Fathiyarizq Mahendra; SANTIYASA, I Wayan. Sentiment Analysis of the Indonesian Health Ministry Performance in Covid-19 Crisis using Support Vector Machine (SVM).JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 10, n. 1, p. 65-72, aug. 2021. ISSN 2654-5101. Available at: https://ojs.unud.ac.id/index.php/JLK/article/view/64433. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JLK.2021.v10.i01.p09.

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ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 10 No 1 (2021): JELIKU Volume 10 No 1, Agustus 2021

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License