Analisa Sentimen Masyarakat Dalam Penggunaan Vaksin Sinovac Dengan Menerapkan Algoritma Term Frequence – Inverse Document Frequence (TF-IDF) dan Metode Deskripsi
- Natalia Silalahi Universitas Budi Darma, Medan, Indonesia
-
Guidio Leonarde Ginting
*
Universitas Budi Darma, Medan,
Indonesia
(*) Corresponding Author
Abstract
Socialmedia is a medium used by Indonesian people to socialize and also as a medium to express their thoughts on something. Communities who support and reject the procurement of the Sinovac vaccine carried out by the Indonesian government based on the responses submitted by the community on Twitter regarding the procurement of the corona virus vaccine, it can be known in general how much community support, rejects or is neutral on the procurement of the Sinovac corona virus vaccine. using RapidMiner 9.0 using the search operator twitter, then processing the data with the Text Mining Algorithm as a preprocessing text mining, then as a weighting using the Term Frequency – Inverse Document Frequency (TF-IDF) algorithm. The results of data processing will be tested using RapidMiner using several operators such as replace, subprocess and analyze sentiment, the results of the two will be compared with the Term Frequency - Inverse Document Frequency (TF-IDF) algorithm that can only determine sentences with negative or positive meanings, but the ability depends on to the negative and positive connotation words used and the description method is able to describe the general sentiment so that the results of the weighting with the Term Frequency – Inverse Document Frequency (TF-IDF) algorithm can be understood. Therefore, more appropriate connotations are needed in the topic of vaccination. The results of this study can provide an overview of the proportion of people who support, reject or are neutral in procuring the Sinovac vaccine, then the percentage of these results is tested using the RapidMinner application and produces the same percentage value.
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Pages: 206-217
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