PRASETYA, IRFAN (2021) SENTIMEN ANALISIS TINGKAT KEPERCAYAAN MASYARAKAT TERHADAP VAKSINASI COVID 19 MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The corona virus has become a world pandemic and has spread almost all over the world, includ-ing Indonesia. There are many negative impacts caused by the spread of covid 19 in Indonesia, so that the goverenment takes steps to prevent the spread of the corona virus by vaccination the community. Responses to the level of public trust in vaccination are quite diverse on social media, especially Instagram, some agree, some don't agree and some don't really care about the vaccines. This study aims to analyze public opinion on the COVID-19 vaccination. The data collection pro-cess in this study uses the Web Scrapping Technique, namely by using the help of tools on the browser extension called Phantom Buster and then copying the url on the @kemenkes_ri post then the comment data on the post is downloaded in xls format. In this study, the data used were 1200 comment data with 90% training data and 10% test data. The data is classified using the naive bayes classifier method by testing the accuracy level calculation using a confusion matrix with threshold and accuracy parameters. Based on the results of the highest accuracy obtained on test-ing 90% of training data and 10% of test data with a threshold value of 2 with the highest accura-cy of 95.83%. It can be concluded that Nave Bayes succeeded in conducting sentiment analysis on the covid 19 vaccination with an accuracy rate of 95.83% with a threshold value of 2. Keywords: Sentiment Analysis, Social media, Vaccinattion, Web Scrapping, Naïve Bayes Classifi-er, Confusion Matrix. Virus corona telah menjadi pandemi dunia dan sudah menyebar hampir ke seluruh dunia, termasuk Indonesia. Banyak dampak negatif yang diakibatkan oleh penyebaran covid 19 di Indonesia, sehingga pemerintah melakukan tindakan pencegahan terhadap penyebaran virus corona dengan melakukan vaksinasi kepa-da masyarakat. Tanggapan kepercayaan masyarakat terhadap vaksinasi cukup be-ragam di Media sosial khususnya Instagram, ada yang setuju, ada yang tidak set-uju dan ada juga yang tidak terlalu mempermasalahkan tentang vaksinasi tersebut. Penelitian ini bertujuan untuk menganalisis pendapat masyarakat terhadap vaksi-nasi covid 19. Proses pengambilan data pada penelitian ini menggunakan Tekhnik Web Scrapping yaitu dengan cara menggunakan bantuan tools pada browser ex-tention yang bernama phantom buster lalu menyalin url pada postingan @kemenkes_ri kemudian data komentar pada postingan tersebut di unduh dengan format xls. Pada penelitian ini data yang di gunakan sebanyak 1200 data komentar dengan pembagian data latih 90 % dan data uji sebanyak 10 %. Data tersebut diklasifikasi-kan menggunakan metode naive bayes classifier dengan pengujian perhitungan tingkat akurasi menggunakan confusion matrix dengan parameter threshold dan akurasi. Berdasarkan Hasil akurasi tertinggi didapatkan pada pen-gujian 90 % data latih dan 10% data uji dengan nilai threshold 2 dengan akurasi tertinggi 95,83 %. Dapat disimpulkan bahwa naïve bayes berhasil melakukan ana-lisis sentimen terhadap vaksinasi covid 19 dengan tingkat akurasi 95,83% dengan nilai threshold 2. Kata kunci: Sentimen Analisis, Media Sosial, Vaksinasi, Web Scrapping, Naïve Bayes Classifier, Confusion Matrix.
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41516320023 |
| Uncontrolled Keywords: | Sentimen Analisis, Media Sosial, Vaksinasi, Web Scrapping, Naïve Bayes Classifier, Confusion Matrix. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 24 Oct 2024 02:43 |
| Last Modified: | 24 Oct 2024 02:43 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/49641 |
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