Analisa Sentimen Tingkat Kepuasan Terhadap Pemberian Vaksin Menggunakan Algoritma Machine Learning

SIHOMBING, DANIEL (2022) Analisa Sentimen Tingkat Kepuasan Terhadap Pemberian Vaksin Menggunakan Algoritma Machine Learning. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Abstract – Covid-19 is a virus originating from China, Wuhan, the Covid-19 virus can be transmitted through the air, touch and attack the respiratory system due to symptoms of high fever and loss of sense of smell and taste so many governments have locked down and started looking for solutions to the virus. , This virus is one of the biggest epidemics that has an impact on several countries. Many countries have started testing vaccine manufacturing including, AstraZeneca, Sinovac, to prevent more and more spread, so the purpose of this study was to determine the sentiment of satisfaction level with vaccine administration. by using the Support Vector Machine algorithm and Naïve bayes , which can conclude the response from the impact of the vaccine so that people can assume that the vaccine is very good and safe to prevent the spread of the Covid-19 virus, but many people do not give a good response to the implementation of vaccination in Indonesia. every area starting from the impact of pain in the chest, fever that does not go down, making many people do not want to be vaccinated, the government urges that people who do not vaccinate their activity level will be limited to avoid the spread of the virus, this Support Vector Machine method used as an intermediary to find out each level of responsiveness of the community in an effort to make a benchmark that vaccines are feasible to prevent the Covid-19 Virus. In this study, the author will use Machine Learning with the application of the Support Vector Machine Algorithm to be able to see the level of satisfaction for people who receive vaccinations for the Sinovac and AstraZeneca vaccines from the response data obtained through twitter data crawling then processed into csv data and a tokenizing filter is performed for each word. used to find responses about the vaccine and to compare sentiments of both positive and negative responses. Key words: research, Covid-19, SINOVAC, AstraZeneca,Machine Learning, Multinominal Naïve Baye Abstrak – Covid-19 merupakan Virus yang berasal dari China, Wuhan, Virus Covid-19 dapat tertular melalui udara, sentuhan dan menyerang sistem pernafasan dampak gejala demam tinggi serta kehilangan indra penciuman dan perasa sehingga banyak pemerintah melakukan lockdown dan mulai mencari solusi terhadap Virus tersebut, Virus ini salah satu wabah terbesar yang berdampak pada bagian beberapa Negara, Banyak Negara yang sudah mulai melakukan uji coba pembuatan Vaksin diantaranya, AstraZeneca, Sinovac, untuk mencegah penyebaran yang semakin banyak, maka tujuan penelitian ini dibuat untuk mengetahui Sentimen Tingkat Kepuasan Terhadap Pemberian Vaksin dengan menggunakan algoritma Support Vector Machine dan Naïve Bayes yang akan dapat disimpulkan respon dari dampak Vaksin tersebut sehingga masyarakat dapat beranggapan bahwa Vaksin tersebut sangat baik dan aman untuk mencegah penyebaran Virus Covid-19 namun tak luput dari banyak masyarakat kurang memberikan tanggapan yang baik terhadap pemberlakuan Vaksinasi disetiap daerah mulai dari dampak rasa nyeri pada bagian dada, demam yang kian tak kunjung turun membuat banyak masyarakat tidak ingin diberlakukan vaksinasi, pemerintah menghimbau bahwa masyarakat yang tidak melakukan vaksin maka akan dibatasi tingkat aktivitas agar terhindar dari penhyebaran virus tersebut, Metode Support Vector Machine ini digunakan sebagai perantara untuk mengetahui tiap tingkatan responsive dari masyarakat upaya menjadikan patokan bahwa Vaksin layak untuk mencegah Virus Covid-19. Pada penelitian ini penulis akan menggunakan Machine Learning dengan Algoritma Support Vector Machine dan Naïve Bayes penerapan untuk dapat melihat tingak kepuasa bagi masyarakat yang menerima vaksinasi jenis Vaksin Sinovac dan AstraZeneca dari respon data yang didapat melalui crawling data twitter kemudian diolah menjadi data csv dan dilakukan Tokenizing filter tiap kata yang digunakan untuk mencari tanggapan mengenai Vaksin tersebut serta melakukan perbandingan sentiment baik respon positif dan negatif. Kata kunci: penelitian, Covid-19, SINOVAC, AstraZeneca, Machine Learning, Multinominal Naïve Bayes

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