Analisis Sentimen Pada Isu Vaksin Covid-19 di Indonesia dengan Metode Naive Bayes Classifier

Abstract

The Covid-19 pandemic that has occurred in Indonesia and even in the world has not yet ended. Various efforts have been made by the Indonesian government to minimize the spread of this virus, such as the implementation of a lockdown, Large-Scale Social Restrictions (PSBB), a ban on going home during the Eid al-Fitr holiday, and so on. One of the new policies issued by the government is the vaccination program, where the government has started implementing the program since early 2021 for the people of Indonesia, which aims to increase antibodies to avoid exposure to the Covid-19 virus. To find out opinions, comments, or feedback given by the public on this new policy, sentiment analysis can be done. The process of this sentiment analysis includes data collection, namely the crawled tweet data originating from the Twitter social media. The data is then selected for further pre-processing stage so that the data is clean and ready for classification. Furthermore, sentiment weighting is carried out for data labeling using a lexicon dictionary and negative words. Then after that, the terms or words are weighted with tf-idf and followed by the feature selection process using Information Gain. Furthermore, the classification process is carried out using the Naive Bayes Classifier algorithm to classify the data into 3 classes, namely positive, negative, and neutral sentiments. The results of this study are to produce a model accuracy rate of 78%, recall 80%, and an AUC score of 0.904.

Naïve Bayes and TF-IDF for Sentiment Analysis of the Covid-19 Booster Vaccine

“…The reason for dividing the data is 60:40 because it refers to another study [9] with quite good results, and there is a belief that using this division will increase the results.…”

Analisis Sentimen Terhadap Opini Patriarki Menggunakan Metode Naïve Bayes

“…Namun, Naive Bayes sensitif terhadap sejumlah besar fitur, yang menyebabkan kesalahan klasifikasi. Untuk mengatasi hal ini, makalah penelitian mengusulkan penggunaan tf-idf sebagai metode pemilihan fitur untuk meningkatkan kinerja Naïve Baye [10].…”

Analisis Percepatan Pemulihan Ekonomi Indonesia Pasca Pandemi Dengan Big Data Dan Deep Learning

“…Pendekatan ini memanfaatkan daftar kata kunci atau kamus (lexicon) yang telah terkategori, seperti positif atau negatif. Saat menganalisis teks, sistem mencocokkan kata-kata dalam teks dengan daftar kata kunci tersebut untuk menetapkan sentimen atau klasifikasi yang sesuai [20]. Pendekatan ini mengandalkan kamus kata kunci sebelumnya untuk memberi label pada data.…”