Public Sentiment Analysis Towards Astrazeneca Vaccine in Indonesia Using Multinomial Naive Bayes

Nur Qodariyah Fitriyah; Guruh Wijaya; M. Fadhil Al Hikam Tirta Bayu  Aj; Hardian  Oktavianto

doi:10.46229/jifotech.v5i2.1007

Penulis

Nur Qodariyah Fitriyah Universitas Muhammadiyah Jember
Guruh Wijaya Universitas Muhammadiyah Jember
M. Fadhil Al Hikam Tirta Bayu Aj Universitas Muhammadiyah Jember
Hardian Oktavianto Universitas Muhammadiyah Jember

DOI:

https://doi.org/10.46229/jifotech.v5i2.1007

Kata Kunci:

analisis sentimen, astrazeneca, multinomial naive bayes, twitter

Abstrak

Virus COVID-19 yang pertama kali muncul di Wuhan pada tahun 2019 telah menyebar ke seluruh dunia, mendorong berbagai upaya vaksinasi meskipun efektivitas jangka panjang vaksin masih menjadi perdebatan. Pemerintah Indonesia berupaya mempercepat program vaksinasi dengan mengamankan jutaan dosis vaksin, namun respons masyarakat beragam, terutama terkait kekhawatiran terhadap aspek keamanan, efektivitas, dan pertimbangan keagamaan. Penelitian ini bertujuan untuk menganalisis opini publik mengenai vaksin COVID-19, khususnya vaksin AstraZeneca, melalui data sentimen di platform Twitter. Data dikumpulkan menggunakan Twitter API, kemudian melalui proses pembersihan dan praproses, termasuk tokenisasi, penghapusan stopword, dan stemming. Selanjutnya, data dikonversi ke dalam bentuk numerik menggunakan metode TF-IDF sebelum diklasifikasikan menggunakan algoritma Multinomial Naive Bayes. Kinerja model dievaluasi berdasarkan metrik akurasi, presisi, recall, dan F1-score untuk mengidentifikasi sentimen positif, negatif, atau netral. Hasil penelitian menunjukkan bahwa model Multinomial Naive Bayes mampu mengklasifikasikan sentimen publik terhadap efek samping vaksin AstraZeneca dengan akurasi 86,91%, presisi 89,26%, dan recall 85,26%. Temuan ini menunjukkan bahwa model tersebut efektif dalam mengidentifikasi sentimen positif dengan tingkat ketepatan yang tinggi.

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