Tweets Sentiment on PPKM Policy as a Covid-19 Response in Indonesia

Hansun, Seng; Suryadibrata, Alethea; Nurhasanah, Rossy; Fitra, Jaka

doi:10.21817/indjcse/2022/v13i1/221301302

Cited by 6 publications

(4 citation statements)

References 13 publications

(13 reference statements)

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“…In this study, the maximum number of trials is set at 50. The following parameters, based on previous studies [ 18 , 22 ], ranges are used:…”

Section: Methodsmentioning

confidence: 99%

“…Based on the mean squared error (MSE) indicator, the authors’ highlighted the potential of Stacked LSTM compared to CNN-LSTM and Bi-LSTM for adaptive and multi-step gold price forecasting. Hansun & Suryadibrata [ 18 ] utilized a simple three-layer LSTM to predict gold price during the COVID-19 crisis using 5000 data. The LSTM parameters, such as the number of hidden units, batch size, and dropout probability are selected using the trial and error approach.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning

Amini,

Kalantari

2024

PLoS ONE

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Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

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“…In this study, the maximum number of trials is set at 50. The following parameters, based on previous studies [ 18 , 22 ], ranges are used:…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning

Amini,

Kalantari

2024

PLoS ONE

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“…If it is discovered that the policy causes social unrest, the government must respond. The public's opinion of a policy can be seen through the comments that appear in the mass media [10]. One of them is Twitter, a social media site.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Long Short-Term Memory for Analysis of Public Opinion Sentiment on Government Policy During the COVID-19 Pandemic

Yulita,

Al-Auza’i,

Prabuwono

et al. 2023

IJACSA

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One of the initiatives adopted by the Indonesian government to combat the development of COVID-19 in Indonesia is Community Activities Restrictions Enforcement. Many public opinions emerged, both for and against this policy. There are so many comments every second that it is certainly not easy to analyze them by reading each one by one. This task necessitates computer applications. Therefore, this study was conducted to produce an application that can help analyze public sentiment on the policy through social media, namely Twitter, into three classes: positive, neutral, and negative. The method used in this research is bidirectional long short-term memory (BiLSTM), one of the algorithms of deep learning. This study trains the model using the dataset, which consists of 10,486 tweets. The model receives an f1-score of 76.67 %. Thus, the model can be used to analyze public sentiment when the same policy is enforced. It can determine public acceptance of this policy. Thus, the system created in this research can be used as evaluation material for the government to review the policy when it is implemented in the future. However, this study concentrates on how to develop the sentiment analysis system and does not examine how the community responds to government policy.

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“…In addition to the machine learning approach, deep learning works by studying data features to generate sophisticated predictions and has been popularly used in sentiment analysis in recent years [14]. Utilization of deep learning using the LSTM algorithm for sentiment analysis regarding PPKM policies during the Covid-19 pandemic in Indonesia obtained very good accuracy reaching 92.59% [15].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM)

Kristiyanti¹,

Hardani²

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac, Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax, often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January 2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19, #vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.

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Tweets Sentiment on PPKM Policy as a Covid-19 Response in Indonesia

Cited by 6 publications

References 13 publications

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning

Bidirectional Long Short-Term Memory for Analysis of Public Opinion Sentiment on Government Policy During the COVID-19 Pandemic

Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM)

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