Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X
The objective of the research is to examine the public opinion regarding the 2024 Indonesian election results by applying Naïve Bayes to social media data obtained from platform X of Twitter. A dataset comprising 2,500 election-related tweets was obtained by web scraping and then subjected to tokeni...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Islamic University of Indragiri
2024-11-01
|
| Series: | Sistemasi: Jurnal Sistem Informasi |
| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/4592 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841555743498567680 |
|---|---|
| author | Ahmad Syakir Muliana Dinda Lestarini Sarifah Putri Raflesia |
| author_facet | Ahmad Syakir Muliana Dinda Lestarini Sarifah Putri Raflesia |
| author_sort | Ahmad Syakir Muliana |
| collection | DOAJ |
| description | The objective of the research is to examine the public opinion regarding the 2024 Indonesian election results by applying Naïve Bayes to social media data obtained from platform X of Twitter. A dataset comprising 2,500 election-related tweets was obtained by web scraping and then subjected to tokenization, stopword elimination, stemming, and TF-IDF weighting for preprocessing. The application of the Synthetic Minority Oversampling Technique (SMOTE) was attempted to mitigate class imbalance. The performance of the Naïve Bayes model was assessed using Stratified K-Fold Cross-Validation. The model achieved an average accuracy of 66.90% on the test set and 80% during cross-validation. The results demonstrate successful categorization of positive sentiment, although the model encountered difficulties in precisely detection of negative and neutral sentiments. The results underscore significant consequences for policymakers and political parties in formulating effective communication strategies. Further study is advised to investigate sophisticated algorithms to improve the accuracy of sentiment classification, namely in detecting neutral sentiments. |
| format | Article |
| id | doaj-art-53be3b3505d74c66b044fcefe44a29dd |
| institution | Kabale University |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2024-11-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-53be3b3505d74c66b044fcefe44a29dd2025-01-08T03:10:27ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192024-11-011362467247810.32520/stmsi.v13i6.4592903Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media XAhmad Syakir Muliana0Dinda Lestarini1Sarifah Putri Raflesia2Universitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaThe objective of the research is to examine the public opinion regarding the 2024 Indonesian election results by applying Naïve Bayes to social media data obtained from platform X of Twitter. A dataset comprising 2,500 election-related tweets was obtained by web scraping and then subjected to tokenization, stopword elimination, stemming, and TF-IDF weighting for preprocessing. The application of the Synthetic Minority Oversampling Technique (SMOTE) was attempted to mitigate class imbalance. The performance of the Naïve Bayes model was assessed using Stratified K-Fold Cross-Validation. The model achieved an average accuracy of 66.90% on the test set and 80% during cross-validation. The results demonstrate successful categorization of positive sentiment, although the model encountered difficulties in precisely detection of negative and neutral sentiments. The results underscore significant consequences for policymakers and political parties in formulating effective communication strategies. Further study is advised to investigate sophisticated algorithms to improve the accuracy of sentiment classification, namely in detecting neutral sentiments.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/4592 |
| spellingShingle | Ahmad Syakir Muliana Dinda Lestarini Sarifah Putri Raflesia Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X Sistemasi: Jurnal Sistem Informasi |
| title | Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X |
| title_full | Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X |
| title_fullStr | Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X |
| title_full_unstemmed | Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X |
| title_short | Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X |
| title_sort | analysis of public sentiment on election results using naive bayes in social media x |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/4592 |
| work_keys_str_mv | AT ahmadsyakirmuliana analysisofpublicsentimentonelectionresultsusingnaivebayesinsocialmediax AT dindalestarini analysisofpublicsentimentonelectionresultsusingnaivebayesinsocialmediax AT sarifahputriraflesia analysisofpublicsentimentonelectionresultsusingnaivebayesinsocialmediax |