Text Mining Literature Review on Indonesian Social Media

Thenata, Angelina Pramana

doi:10.26418/jp.v7i2.47975

Cited by 1 publication

(2 citation statements)

References 19 publications

(28 reference statements)

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“…The discovery of interesting knowledge is obtained from data in the form of texts. Machine learning works with various algorithms such as decision trees, naïve Bayes, KNN, RF, and SVM [10].…”

Section: Machine Learningmentioning

confidence: 99%

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Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

Dionisia Bhisetya Rarasati,

Angelina Pramana Thenata,

Afiyah Salsabila Arief

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.

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Section: Machine Learningmentioning

confidence: 99%

“…This stage prepares data so the machine can analyze them easily [10]. There are several stages: tokenizing, removing stopword, and stemming.…”

Section: B Text Preprocessing Stagementioning

confidence: 99%