PERBANDINGAN KLASIFIKASI NAIVE BAYES DENGAN ATAU TANPA PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI FITUR PADA ANALISIS SENTIMENT TERHADAP PERPRES NO 10 TAHUN 2021 TENTANG INVESTASI MIRAS PADA MEDIA SOSIAL TWITTER
Social media is one of the forms of information and communication technology development. Social networking is a space where you can communicate with each other, and one of the largest social media platforms is Twitter. President Joko Widodo issued Presidential Regulation No. 10 of 2021 regarding in...
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2023-01-23.
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| 100 | 1 | 0 | |a Gilbert Panangian Doloksaribu, . |e author |
| 245 | 0 | 0 | |a PERBANDINGAN KLASIFIKASI NAIVE BAYES DENGAN ATAU TANPA PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI FITUR PADA ANALISIS SENTIMENT TERHADAP PERPRES NO 10 TAHUN 2021 TENTANG INVESTASI MIRAS PADA MEDIA SOSIAL TWITTER |
| 260 | |c 2023-01-23. | ||
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| 500 | |a http://repository.upnvj.ac.id/24060/10/LAMPIRAN.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/24060/20/KARYA%20ILMIAH.pdf | ||
| 520 | |a Social media is one of the forms of information and communication technology development. Social networking is a space where you can communicate with each other, and one of the largest social media platforms is Twitter. President Joko Widodo issued Presidential Regulation No. 10 of 2021 regarding investment in the alcoholic beverage industry, which invited both pro and contra opinions. This research was conducted to classify public comments on Presidential Regulation No. 10 of 2021 using the Naive Bayes classification method. The tweet data obtained by crawling on Twitter with related keywords were transformed from unstructured data to structured data, and then the evaluation values between classifications were compared using Naïve Bayes with Naive Bayes classification using Particle Swarm Optimization (PSO) as feature selection to reduce irrelevant attributes in the classification process, thereby improving the accuracy value of the dataset. This study resulted in Naive Bayes classification accuracy values of 65% and Naive Bayes classification with PSO of 69% using iteration parameters 100 times. Evaluation measurements were performed using the Confusion Matrix method and showed an increase in accuracy and F1-Score values using PSO as the feature selection attribute. | ||
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