印度尼西亚实施加强疫苗的公众情绪的k近邻分析

Ihwana As'ad, Muhammad Arfah Asis, Hariani Ma'tang Pakka, Randi Mursalim, Yusnita binti Muhamad Noor
{"title":"印度尼西亚实施加强疫苗的公众情绪的k近邻分析","authors":"Ihwana As'ad, Muhammad Arfah Asis, Hariani Ma'tang Pakka, Randi Mursalim, Yusnita binti Muhamad Noor","doi":"10.33096/ilkom.v15i2.1561.365-372","DOIUrl":null,"url":null,"abstract":"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%.","PeriodicalId":33690,"journal":{"name":"Ilkom Jurnal Ilmiah","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia\",\"authors\":\"Ihwana As'ad, Muhammad Arfah Asis, Hariani Ma'tang Pakka, Randi Mursalim, Yusnita binti Muhamad Noor\",\"doi\":\"10.33096/ilkom.v15i2.1561.365-372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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%.\",\"PeriodicalId\":33690,\"journal\":{\"name\":\"Ilkom Jurnal Ilmiah\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ilkom Jurnal Ilmiah\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33096/ilkom.v15i2.1561.365-372\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ilkom Jurnal Ilmiah","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33096/ilkom.v15i2.1561.365-372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了防止COVID-19在印度尼西亚的传播,印度尼西亚共和国政府自2022年1月12日起实施了一项加强疫苗计划,优先为老年人和弱势群体以及接种第二次C-19疫苗超过6个月的人接种。这项计划的实施在公众中引起了许多赞成和反对的声音,这些声音通过社交媒体表达出来,有积极的,也有消极的。因此,需要情感分析来检验这些现象。本研究旨在采用k近邻法确定公众的正面和负面反应。总共收集了2000条评论数据,并根据正面和负面情绪依次进行分类。500条评论作为训练数据,平均分为正负两类,每类包含250个数据。使用K = 9的值,结果显示43%的人持积极态度,57%的人持消极态度。采用10重交叉验证进行效度检验,准确率为82.60%,查全率为82.60%,查全率为83.89%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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. 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%.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
4 weeks
期刊最新文献
K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia Feature Space Augmentation for Negation Handling on Sentiment Analysis Diabetes Mellitus Early Detection Simulation using The K-Nearest Neighbors Algorithm with Cloud-Based Runtime (COLAB) Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1