Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X)
This study compares the performance of the K-Nearest Neighbors (K-NN) and Naive Bayes Classifier (NBC) algorithms in sentiment analysis of the 2024 Regional Election (Pilkada) based on Indonesian local data sourced from platform X. A total of 1,187 tweets were collected through crawling, followed by...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9593 |
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| author | Lola Enjelia Yana Cahyana Rahmat Deden Wahiddin |
| author_facet | Lola Enjelia Yana Cahyana Rahmat Deden Wahiddin |
| author_sort | Lola Enjelia |
| collection | DOAJ |
| description | This study compares the performance of the K-Nearest Neighbors (K-NN) and Naive Bayes Classifier (NBC) algorithms in sentiment analysis of the 2024 Regional Election (Pilkada) based on Indonesian local data sourced from platform X. A total of 1,187 tweets were collected through crawling, followed by extensive preprocessing and manual sentiment labeling by a professional linguist to ensure data validity and reliability. The study highlights NBC's superior accuracy (81.05%) compared to K-NN (75.26%), largely due to the characteristics of short-text social media data that align with NBC's independence assumptions. Key terms identified through TF-IDF analysis include “pilkada”, “2024”, and “damai” in positive sentiment, while “mahkamah konstitusi” and “kalah” dominated negative sentiment. The results imply that although public discourse largely supports the election process, critical sentiments toward election dispute issues persist. These findings offer practical implications for election authorities, policymakers, and digital campaign strategists, particularly in optimizing public communication strategies, early detection of potential conflicts, and designing public opinion monitoring systems based on real-time sentiment analysis. By leveraging high-quality labeled local data, this study makes a significant contribution to modeling public opinion dynamics in Indonesia during political events. |
| format | Article |
| id | doaj-art-0bfba6f1bcc04b5aa731ec90ff3ca4a5 |
| institution | Matheson Library |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-0bfba6f1bcc04b5aa731ec90ff3ca4a52025-07-29T01:34:47ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-019394695410.30871/jaic.v9i3.95937138Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X)Lola EnjeliaYana CahyanaRahmatDeden WahiddinThis study compares the performance of the K-Nearest Neighbors (K-NN) and Naive Bayes Classifier (NBC) algorithms in sentiment analysis of the 2024 Regional Election (Pilkada) based on Indonesian local data sourced from platform X. A total of 1,187 tweets were collected through crawling, followed by extensive preprocessing and manual sentiment labeling by a professional linguist to ensure data validity and reliability. The study highlights NBC's superior accuracy (81.05%) compared to K-NN (75.26%), largely due to the characteristics of short-text social media data that align with NBC's independence assumptions. Key terms identified through TF-IDF analysis include “pilkada”, “2024”, and “damai” in positive sentiment, while “mahkamah konstitusi” and “kalah” dominated negative sentiment. The results imply that although public discourse largely supports the election process, critical sentiments toward election dispute issues persist. These findings offer practical implications for election authorities, policymakers, and digital campaign strategists, particularly in optimizing public communication strategies, early detection of potential conflicts, and designing public opinion monitoring systems based on real-time sentiment analysis. By leveraging high-quality labeled local data, this study makes a significant contribution to modeling public opinion dynamics in Indonesia during political events.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9593sentiment analysisk-nearest neighbor (knn)naive bayes2024 electiontwitter (x) |
| spellingShingle | Lola Enjelia Yana Cahyana Rahmat Deden Wahiddin Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) Journal of Applied Informatics and Computing sentiment analysis k-nearest neighbor (knn) naive bayes 2024 election twitter (x) |
| title | Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) |
| title_full | Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) |
| title_fullStr | Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) |
| title_full_unstemmed | Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) |
| title_short | Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) |
| title_sort | comparison of k nearest neighbors and naive bayes classifier algorithms in sentiment analysis of 2024 election in twitter x |
| topic | sentiment analysis k-nearest neighbor (knn) naive bayes 2024 election twitter (x) |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9593 |
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