Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors
Using Twitter as a platform for sharing information includes tracking public perceptions of the performance of the Indonesian National Police (POLRI). Public sentiment assists as a gauge for evaluating POLRI's operational capabilities and supports decision-making processes to enhance the organi...
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2024-06-01
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| Series: | IJID (International Journal on Informatics for Development) |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4500 |
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| _version_ | 1849700991049400320 |
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| author | Yusuf Handika Isa Faqihuddin Hanif Firman Noor Hasan |
| author_facet | Yusuf Handika Isa Faqihuddin Hanif Firman Noor Hasan |
| author_sort | Yusuf Handika |
| collection | DOAJ |
| description | Using Twitter as a platform for sharing information includes tracking public perceptions of the performance of the Indonesian National Police (POLRI). Public sentiment assists as a gauge for evaluating POLRI's operational capabilities and supports decision-making processes to enhance the organization's reputation. However, raw public opinion data often requires careful analysis for decision-making. Hence, conducting sentiment analysis of Twitter data is crucial. This analytical process involves extracting and classifying opinions into neutral, positive, and negative sentiments. This study employs two distinct sentiment analysis methods: the Naive Bayes algorithm and the K-Nearest Neighbors. Analysis of 1285 tweets reveals prevailing satisfaction with POLRI's performance, indicated by many positive sentiments. However, there is also a notable number of negative feelings. The assessment from confusion matrix results demonstrate that the Naive Bayes algorithm achieves 99.03% accuracy, while the K-Nearest Neighbors algorithm achieves 95.33% accuracy. By leveraging insights from public opinion data, POLRI can make more accurate and timely decisions, enabling it to better fulfill the community's needs and expectations. This strategic use of data enhances service quality and bolsters POLRI's favorable image among the public fosters more harmonious relationships and enhances public trust in law enforcement agencies. |
| format | Article |
| id | doaj-art-2b31cd56b09040c0a1a5c9130e768a61 |
| institution | DOAJ |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-2b31cd56b09040c0a1a5c9130e768a612025-08-20T03:18:05ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482024-06-0113138639910.14421/ijid.2024.45004124Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest NeighborsYusuf Handika0Isa Faqihuddin Hanif1Firman Noor Hasan2https://orcid.org/0000-0002-1246-3462Universitas Muhammadiyah Prof. Dr. HamkaUniversitas Muhammadiyah Prof. Dr. HamkaUniversitas Muhammadiyah Prof. Dr. HamkaUsing Twitter as a platform for sharing information includes tracking public perceptions of the performance of the Indonesian National Police (POLRI). Public sentiment assists as a gauge for evaluating POLRI's operational capabilities and supports decision-making processes to enhance the organization's reputation. However, raw public opinion data often requires careful analysis for decision-making. Hence, conducting sentiment analysis of Twitter data is crucial. This analytical process involves extracting and classifying opinions into neutral, positive, and negative sentiments. This study employs two distinct sentiment analysis methods: the Naive Bayes algorithm and the K-Nearest Neighbors. Analysis of 1285 tweets reveals prevailing satisfaction with POLRI's performance, indicated by many positive sentiments. However, there is also a notable number of negative feelings. The assessment from confusion matrix results demonstrate that the Naive Bayes algorithm achieves 99.03% accuracy, while the K-Nearest Neighbors algorithm achieves 95.33% accuracy. By leveraging insights from public opinion data, POLRI can make more accurate and timely decisions, enabling it to better fulfill the community's needs and expectations. This strategic use of data enhances service quality and bolsters POLRI's favorable image among the public fosters more harmonious relationships and enhances public trust in law enforcement agencies.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4500algorithmconfusion matrixdecision makingsentiment analysistwitter |
| spellingShingle | Yusuf Handika Isa Faqihuddin Hanif Firman Noor Hasan Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors IJID (International Journal on Informatics for Development) algorithm confusion matrix decision making sentiment analysis |
| title | Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors |
| title_full | Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors |
| title_fullStr | Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors |
| title_full_unstemmed | Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors |
| title_short | Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors |
| title_sort | analysis of public sentiment towards polri s performance using naive bayes and k nearest neighbors |
| topic | algorithm confusion matrix decision making sentiment analysis |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4500 |
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