Analisis Sentimen Terhadap Vaksin Covid-19 di Indonesia pada Twitter Menggunakan Metode Lexicon Based

Increasing the number of cases of Covid-19 in Indonesia, the Government of Indonesia has made various efforts in handling Covid-19, one of which is the policy of Covid-19 vaccine for all indonesian people. With this policy, many people express their opinions through Twitter. The purpose of this sentiment analysis research is to find out public opinion about the policy of vaccine covid-19 on Twitter whether the opinion falls into the class of sentiment that is categorized into 5 namely very positive sentiment, positive sentiment, negative sentiment, somewhat negative or neutral sentiment and know the accuracy of the percentage of each class of polarity. Lexicon based research method using vader sentiment library. Vader sentiment, which is a lexicon-based method of analysis based on rule-based sentiment analysis. Based on the accuracy of the percentage obtained by positive sentiment as much as 20.25%, somewhat positive as much as 23.9%, negative as much as 1.88%, somewhat negative as much as 9.6% and neutral as much as 44.36%. It can be concluded that public opinion on the Covid-19 vaccine on Twitter using lexicon-based methods by utilizing vader sentiment library is mostly neutral.Meningkatnya jumlah kasus Covid-19 di Indonesia, pemerintah RI telah melakukan berbagai upaya dalam penanganan Covid-19, salah satunya kebijakan vaksin Covid-19 untuk seluruh masyarakat indonesia. Dengan adanya kebijakan tersebut, banyak sekali masyarakat yang menyampaikan opininya melalui Twitter. Tujuan penelitian analisis sentimen ini untuk mengetahui opini masyarakat tentang kebijakan vaksin covid-19 pada twitter apakah opini tersebut masuk kedalam kelas sentimen yang di katagorikan menjadi 5 yaitu sentimen sangat positif, sentimen positif, sentimen negatif, sentimen agak negatif ataupun netral serta mengetahui hasil akurasi persentasi dari setiap kelas polaritas. Metode penelitian lexicon based menggunakan library vader sentiment. Vader sentiment, yaitu metode analisis lexicon-based berbasis rule-based sentiment analysis. Berdasarkan hasil akurasi persentase yang didapat sentimen positif sebanyak 20,25%, agak positif sebanyak 23,9%, negatif sebanyak 1,88%, agak negatif sebanyak 9,6% dan netral sebanyak 44,36%. Dapat disimpulkan bahwa opini masyarakat terhadap vaksin Covid-19 pada Twitter menggunakan metode lexicon based dengan memanfaatkan library vader sentiment kebanyakan adalah bersifat netral