K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia
In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. Th...
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| Formatua: | Artikulua |
| Hizkuntza: | English |
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Prodi Teknik Informatika FIK Universitas Muslim Indonesia
2023
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| Sarrera elektronikoa: | http://umpir.ump.edu.my/id/eprint/41078/1/K-Nearest%20Neighbors%20Analysis%20for%20Public%20Sentiment%20towards%20Implementation%20of%20Booster%20Vaccines%20in%20Indonesia.pdf |
| _version_ | 1825815615023087616 |
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| author | As’ad, Ihwana Asis, Muhammad Arfah Pakka, Hariani Ma’tang Mursalim, Randi Yusnita, Muhamad Noor |
| author_facet | As’ad, Ihwana Asis, Muhammad Arfah Pakka, Hariani Ma’tang Mursalim, Randi Yusnita, Muhamad Noor |
| author_sort | As’ad, Ihwana |
| collection | UMP |
| description | In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. The implementation of this program raised many pros and cons among public which were expressed either positively or negatively through social media. Therefore, sentiment analysis is needed to examine these phenomenons. This study aims to determine the positive and negative response from public by employing K-Nearest Neighbor method. A total of 2,000 commentary data were collected to be in turn classified based on positive and negative sentiments. There are 500 comments used as training data and divided equally to positive and negative class, each consists of 250 data. Using the value of K = 9, the results show a positive sentiment of 43% while a negative sentiment of 57%. Based on the validity test using 10-fold cross validation, an accuracy of 82.60% was obtained, a recall value was 82.60% with a precision of 83.89%. |
| first_indexed | 2024-09-25T03:48:52Z |
| format | Article |
| id | UMPir41078 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:48:52Z |
| publishDate | 2023 |
| publisher | Prodi Teknik Informatika FIK Universitas Muslim Indonesia |
| record_format | dspace |
| spelling | UMPir410782024-04-29T07:46:41Z http://umpir.ump.edu.my/id/eprint/41078/ K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia As’ad, Ihwana Asis, Muhammad Arfah Pakka, Hariani Ma’tang Mursalim, Randi Yusnita, Muhamad Noor QA75 Electronic computers. Computer science In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. The implementation of this program raised many pros and cons among public which were expressed either positively or negatively through social media. Therefore, sentiment analysis is needed to examine these phenomenons. This study aims to determine the positive and negative response from public by employing K-Nearest Neighbor method. A total of 2,000 commentary data were collected to be in turn classified based on positive and negative sentiments. There are 500 comments used as training data and divided equally to positive and negative class, each consists of 250 data. Using the value of K = 9, the results show a positive sentiment of 43% while a negative sentiment of 57%. Based on the validity test using 10-fold cross validation, an accuracy of 82.60% was obtained, a recall value was 82.60% with a precision of 83.89%. Prodi Teknik Informatika FIK Universitas Muslim Indonesia 2023-08 Article PeerReviewed pdf en cc_by_nc_sa_4 http://umpir.ump.edu.my/id/eprint/41078/1/K-Nearest%20Neighbors%20Analysis%20for%20Public%20Sentiment%20towards%20Implementation%20of%20Booster%20Vaccines%20in%20Indonesia.pdf As’ad, Ihwana and Asis, Muhammad Arfah and Pakka, Hariani Ma’tang and Mursalim, Randi and Yusnita, Muhamad Noor (2023) K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia. ILKOM Jurnal Ilmiah, 15 (2). pp. 365-372. ISSN 2087-1716. (Published) http://dx.doi.org/10.33096/ilkom.v15i2.1561.365-372 10.33096/ilkom.v15i2.1561.365-372 |
| spellingShingle | QA75 Electronic computers. Computer science As’ad, Ihwana Asis, Muhammad Arfah Pakka, Hariani Ma’tang Mursalim, Randi Yusnita, Muhamad Noor K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title | K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title_full | K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title_fullStr | K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title_full_unstemmed | K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title_short | K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia |
| title_sort | k nearest neighbors analysis for public sentiment towards implementation of booster vaccines in indonesia |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/41078/1/K-Nearest%20Neighbors%20Analysis%20for%20Public%20Sentiment%20towards%20Implementation%20of%20Booster%20Vaccines%20in%20Indonesia.pdf |
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