Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Rahmawati, Siti; Habibi, Muhammad

doi:10.14421/ijid.2020.09205

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…After the classification model is formed in the training process, the classification process is performed [15]. The research stages are shown in Figure 1.…”

Section: A Modelmentioning

confidence: 99%

“…A naive Bayes classifier is a classification method based on Bayes' theorem. This classifier assumes that the presence of features within a class is independent of other features [15]. Equation ( 1) is a Bayesian formula.…”

Section: Data Modelingmentioning

confidence: 99%

“…The evaluation phase is run to determine the accuracy of the modeling applied to the training data. Then compare the results for two different datasets by applying the confusion matrix and calculating the precision [15].…”

Section: E Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

Sasi¹,

Utami²,

Pramono³

2023

Int.J.Res.Publ.Rev.

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The Russia-Ukraine crisis has not found a solution until now, at least until November 2022. Many Indonesians have expressed their opinions on this matter via social media Twitter. This study uses the Naïve Bayes (NB) Algorithm based on Particle Swarm Optimization (PSO) to analyze opinions regarding the RussiaUkraine war. Data was taken from Twitter using the keywords "Russia Ukraine", "Russia vs Ukraine", "Russia-Ukraine", and "Perang Russia". The number of data sets taken is 5000 data. The results showed that the accuracy of the Naïve Bayes (NB) algorithm without Particle Swarm Optimization (PSO) was 67.72%, the precision value was 58.33%, the recall value was 79.75%, and the error rate of 57.14% while Naïve Bayes (NB) with Particle Swarm Optimization (PSO) the accuracy obtained is 73.48%, the precision value is 65.62%, the recall value is 76.36% and error rate of 50.36%. Thus it can be said that the Particle Swarm Optimization (PSO) applied to the Naïve Bayes algorithm for objects in research can increase the accuracy of results.

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“…After the classification model is formed in the training process, the classification process is performed [15]. The research stages are shown in Figure 1.…”

Section: A Modelmentioning

confidence: 99%

Section: Data Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

Sasi¹,

Utami²,

Pramono³

2023

Int.J.Res.Publ.Rev.

View full text Add to dashboard Cite

show abstract

“…There has already been study done on the Indonesian Social Insurance Administration Organization, such as research on the Public Sentiments Analysis of the Indonesian Social Insurance Administration Organization using Twitter data [2]- [4]. Furthermore, the lexicon-based method is a feature extraction method that has the potential to increase system performance.…”

Section: Abstract a Rt I C L E I N F Omentioning

confidence: 99%

Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches

Rizky Fauzi Akbar,

Habibi

2023

Indo. Jour. on Data Science

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The National Social Security Agency (BPJS) for Employment is the Social Security Administering Agency with the goal of ensuring that each participant or member of the family receives adequate necessities. In its implementation, there is information that is spread, particularly on Twitter, regarding the Ministry of Health's decision, namely regarding Old Age Security (JHT), which can only be distributed/taken after the participant turns 56 years old, causing both pros and cons among the public. Based on unanalyzed tweets on Twitter, it is necessary to do extensive research to collect relevant information based on netizens' viewpoints. This research describes sentiment analysis of tweets from Twitter using the terms JHT, BPJSTK, and BPJS, which yield 4154 data tweets. We employ two approaches in this study: Lexicon Based and Nave Bayes Classifier. According to this study, the accuracy of the testing data is 92% for the Lexicon Based and 95% for the Nave Bayes Classifier. This study concluded that the JHT at BPJS Employment received unfavorable attitudes and negative reactions among users who addressed the rejection of new restrictions where JHT, could only be dispensed or taken when participants at BPJS Employment were 56 years old.

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“…Research [8] is the development of an application to classify comments about recipes from one of the popular food community sites into positive or negative sentiments. Another research is to analyze sentiment regarding the services of the Social Security Administrative Body (BPJS) for Health in Indonesia [9]. This study analyzes tweet sentiment about BPJS services into 4 sentiment classifications, namely satisfied, disappointed, happy and sad.…”

Section: Introductionmentioning

confidence: 99%

Customer Experience Analysis Skincare Products Through Social Media Data Using Topic Modeling and Sentiment Analysis

Habibi

Kusumaningtyas

2023

j. of sci. and appl.eng.

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Currently, skin care products (skincare) are popular among the public. Both men and women are interested in buying skin care products. Moreover, there are many brands of skin care products that are divided into several types of facial and body care, such as moisturizers, toners, cleansers, and masks. Therefore, many consumers take the time to find information, for example, in terms of price, quality, and brand for decision-making. A lot of useful information is in the form of Twitter messages known as tweets which are sent from people who use skin care products because Twitter is one of the online social media where users can share their opinions and experiences. However, consumers still have to spend a lot of time searching, reading, and understanding the comprehensive collection of tweets before buying skin care products.The purpose of this study is to analyze customer experience, analyzing automated tweets about skin care products. Tweets about skin care products will be subjected to a topic modeling process to find out what topics are being discussed. In addition, the topics that have been obtained will be subject to sentiment analysis in the form of positive and negative messages for skin care products. Consumers who are app users don’t waste time reading and analyzing large amounts of data manually and they can decide to buy skin care products more easily.The results of this study obtained 14 topics of discussion related to skincare. Meanwhile, the sentiment analysis results of 14 topics resulted in more positive sentiment class tweets overall. It related the category topic that has the number of tweets to the importance of skincare. In addition, categories related to ingredients for skincare products from nature, namely fruits and spices, are the topics that have the second highest number of tweets. The results of the analysis of tweets related to user experience on Twitter, it was found that users prefer skincare products that use ingredients from nature.

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Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

Cited by 3 publications

References 20 publications

Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches

Customer Experience Analysis Skincare Products Through Social Media Data Using Topic Modeling and Sentiment Analysis

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