dc.contributor.advisor | Herdiyeni, Yeni | |
dc.contributor.advisor | Hardhienata, Medria Kusuma Dewi | |
dc.contributor.author | Chairunnisa, Qarry Atul | |
dc.date.accessioned | 2021-10-01T13:15:54Z | |
dc.date.available | 2021-10-01T13:15:54Z | |
dc.date.issued | 2021-10-01 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/109508 | |
dc.description.abstract | Kebijakan vaksinasi COVID-19 di Indonesia menimbulkan pro dan
kontra. Pemerintah harus mengevaluasi alasan masyarakat yang kontra terhadap
kebijakan tersebut, agar program vaksinasi dapat berjalan dengan lancar. Analisis
sentimen sebagai cara untuk melihat polaritas opini, memungkinkan untuk
mengklasifikasi tanggapan positif, negatif maupun netral di Twitter terkait
kebijakan vaksinasi tersebut. Penelitian ini bertujuan untuk mengetahui tanggapan
masyarakat terhadap vaksinasi COVID-19 dengan melihat distribusi kata dan
membuat model klasifikasi Support Vector Machine (SVM). Analisis sentimen
terdiri dari beberapa tahapan yaitu pengumpulan data, praproses data, pembobotan
data, analisis data, pembagian data, pemodelan klasifikasi, hyperparameter tuning
dan evaluasi model. Model yang dihasilkan menunjukkan performa yang cukup
optimal dalam mengklasifikasi sentimen dengan akurasi, presisi, recall dan
f1-score sebesar 90%. Hasil analisis yang diperoleh ialah berupa gagasan, keluhan
dan saran terhadap vaksinasi COVID-19. | id |
dc.description.abstract | The COVID-19 vaccination policy in Indonesia turns out to be both pros
and cons. The government has to evaluate the underlying reason of why some
people's are against the policy, so that the vaccination program can run smoothly.
Sentiment analysis as a way to see the polarity of opinion, makes it possible to
classify positive, negative or neutral responses on Twitter regarding the
vaccination policy. This study aims to determine the public's response to
COVID-19 vaccination by examining word distribution and creating an Support
Vector Machine (SVM) classification model. Sentiment analysis consists of several
stages, namely data collection, data preprocessing, data weighting, data analysis,
data sharing, classification modeling, hyperparameter tuning and model
evaluation. The results of this study are a model with a relatively optimal
performance in classifying sentiment with an accuracy, precision, recall and
f1-score of 90%. The results of the sentiment analysis obtained are in the form of
ideas, complaints and suggestions for the COVID-19 vaccination. | id |
dc.language.iso | id | id |
dc.publisher | IPB University | id |
dc.title | Analisis Sentimen Pengguna Twitter Terhadap Vaksinasi Covid-19 Di Indonesia Menggunakan Algoritme Support Vector Machine | id |
dc.title.alternative | Sentiment Analysis Of Twitter Users On COVID-19 Vaccination In Indonesia Using Support Vector Machine Algorithm | id |
dc.type | Undergraduate Thesis | id |
dc.subject.keyword | COVID-19 | id |
dc.subject.keyword | Sentiment Analysis | id |
dc.subject.keyword | Support Vector Machine | id |
dc.subject.keyword | Vaccination | id |