Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method
The main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language p...
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| Format: | Article |
| Language: | English |
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2023-12-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/66 |
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| author | Harman Akbar Tullah Muh Akbar Alem Febri Sonni Akbar Iskandar Erwin Gatot Amiruddin Kamaruddin Asnimar |
| author_facet | Harman Akbar Tullah Muh Akbar Alem Febri Sonni Akbar Iskandar Erwin Gatot Amiruddin Kamaruddin Asnimar |
| author_sort | Harman Akbar Tullah |
| collection | DOAJ |
| description | The main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language processing, as well as the application of the Naive Bayes algorithm, this research carefully examines the conversation patterns and topic trends prevalent on Twitter during the CPNS selection phase. The findings of this research unequivocally highlight the enormous influence of Twitter on public sentiment related to CPNS selection, as demonstrated by the classification model's impressive accuracy rate of approximately 95.19%. In addition, this research successfully identifies the influential roles played by key actors, prominent accounts, and narratives in shaping public perceptions. These groundbreaking insights foster a comprehensive understanding of the dynamic nature of public opinion in the context of CPNS selection, providing an invaluable basis for designing more effective communication strategies for the government and prospective civil servants. |
| format | Article |
| id | doaj-art-c27b3df36b81407084af49d4db159765 |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-c27b3df36b81407084af49d4db1597652025-01-28T05:41:12ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082023-12-01132496310.35585/inspir.v13i2.6666Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm MethodHarman Akbar Tullah0Muh Akbar1Alem Febri Sonni2Akbar Iskandar3Erwin Gatot Amiruddin4Kamaruddin5Asnimar6Universitas HasanuddinUniversitas HasanuddinUniversitas HasanuddinUniversitas Teknologi Akba MakassarUniversitas Teknologi Akba MakassarUniversitas Teknologi Akba MakassarUniversitas Teknologi Akba MakassarThe main objective of this research is to uncover the important role played by the social media platform Twitter in shaping public opinion regarding the 2023 Civil Servant Candidate (CPNS) selection process in Indonesia. Using advanced techniques such as social network analysis and Python language processing, as well as the application of the Naive Bayes algorithm, this research carefully examines the conversation patterns and topic trends prevalent on Twitter during the CPNS selection phase. The findings of this research unequivocally highlight the enormous influence of Twitter on public sentiment related to CPNS selection, as demonstrated by the classification model's impressive accuracy rate of approximately 95.19%. In addition, this research successfully identifies the influential roles played by key actors, prominent accounts, and narratives in shaping public perceptions. These groundbreaking insights foster a comprehensive understanding of the dynamic nature of public opinion in the context of CPNS selection, providing an invaluable basis for designing more effective communication strategies for the government and prospective civil servants.https://ojs.unitama.ac.id/index.php/inspiration/article/view/66social network analysiscpnstwittersocial medianaive bayes algorithm |
| spellingShingle | Harman Akbar Tullah Muh Akbar Alem Febri Sonni Akbar Iskandar Erwin Gatot Amiruddin Kamaruddin Asnimar Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method Inspiration social network analysis cpns social media naive bayes algorithm |
| title | Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method |
| title_full | Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method |
| title_fullStr | Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method |
| title_full_unstemmed | Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method |
| title_short | Sentiment Analysis Of Indonesian Civil Servan Candidates 2023 Twitter Network With Naive Bayes Algorithm Method |
| title_sort | sentiment analysis of indonesian civil servan candidates 2023 twitter network with naive bayes algorithm method |
| topic | social network analysis cpns social media naive bayes algorithm |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/66 |
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