Analisis Sentimen Opini Terhadap Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Naïve Bayes Dan Decision Tree

Ahmad, Ahmad Zamsuri; Asril, Elvira; Sadar, Muhamad; Walhidayat, None; Syahtriatna, None; Turnandes, Yogo

doi:10.31849/zn.v5i1.5553

Cited by 4 publications

(6 citation statements)

References 2 publications

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“…Despite this assumption seldom holding in actual data, the algorithm consistently produces robust classification results. The method has been successfully applied in various contexts, such as sentiment analysis on COVID-19 vaccine opinions on Twitter [4], predicting satisfaction in e-KTP service recording [5], and analyzing sentiment on e-Tilang through YouTube [6] [15]. Furthermore, diverse comparisons between classification methods have been conducted.…”

Section: Related Workmentioning

confidence: 99%

“…Previous research by Ahmad et al (2023) conducted Sentiment analysis on COVID-19 vaccine opinions on Twitter social media using the Naïve Bayes and Decision Tree algorithms. The research results show that in general, the public responds positively to the Indonesian government's vaccination policy [4]. Apandi and Sugianto (2019) applied the Naïve Bayes algorithm to predict satisfaction with e-KTP recording services.…”

Section: Introductionmentioning

confidence: 99%

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The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm

Pasaribu,

Sriani

2023

ijsecs

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With the significant growth of internet use in Indonesia, there has been a surge in online business activity. The convenience offered by online platforms is increasingly in demand because it allows consumers to shop without being bound by a certain time or location. Before making a purchase, consumers tend to look for information first through various sources such as reviews on blogs, Instagram, TikTok, or reviews on the YouTube platform which is integrated in the application. This research adopted a method that included planning, literature study, data collection, and data processing using a dataset from the Play Store application which was taken using the Python library with an initial amount of 5000 data. After a manual filtering process which involved removing slang words, eliminating duplications, and normalizing punctuation marks, the remaining data was 3946. The application of the Naïve Bayes algorithm in this research uses probability methods to classify and predict 3141 training data and 805 test data, with Python library help. The accuracy calculation results show satisfactory performance, with an accuracy of 86.00%, precision of 80.74%, recall of 78.13%, and f1-score of 79.00% on a dataset of 3946. Analysis from this research shows the dominance of positive sentiment in 2050 data, while sentiment negative amounted to 1199 data. The amount and quality of training data plays an important role in system predictions, where high data quality provides better accuracy in predicting sentiment classes

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm

Pasaribu,

Sriani

2023

ijsecs

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“…Sistem kesehatan di seluruh dunia dipaksa berubah dalam waktu singkat untuk menangani masalah pandemi global yang muncul setelah terakhir tercatat sejarah pada tahun 1920 (flu spanyol) serta mengambil langkah-langkah penanganan penanganan terhadap pasien baik yang terinfeksi oleh virus COVID-19 maupun penderita penyakit lainnya [2]. Hal ini disebabkan oleh karakteristik dari virus COVID-19 yang sangat mudah menular melalui udara dan kontak antar manusia [3]. Salah satu penanganan yang dilakukan, penyelenggara kesehatan di beberapa negara mulai mencanangkan untuk menggunakan telemedicine sebagai media untuk menyelenggarakan pelayanan kesehatan antara petugas medis dan pasien, khususnya pasien yang tidak harus mendapatkan penanganan yang cukup serius [2] [4].…”

Section: Pendahuluanunclassified

Analisis Teks Pemberitaan Telemedicine di Indonesia: Pendekatan Sentimen, NER, Topic Modeling, dan Social Network dalam Memahami Isu dan Persepsi

Panuntun

Krismawati

Pramana³

et al. 2023

INOHIM

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Telemedicine is becoming an increasingly relevant phenomenon in the health sector in Indonesia, especially with the emergence of the COVID-19 Pandemic. This study examines text analysis of telemedicine news coverage during the COVID-19 pandemic in Indonesia using sentiment analysis, Named Entity Recognition (NER), topic modeling, and Social Network Analysis (SNA). This research aims to gain an in-depth understanding of issues, public perceptions, social networks, and topics related to the use of telemedicine in dealing with a pandemic. This study provides a comprehensive understanding of telemedicine coverage during the COVID-19 pandemic in Indonesia by combining four methods. The findings of this research can provide valuable insights for stakeholders in optimizing the use of telemedicine, understanding public perceptions, and building effective collaborations in handling pandemics.Keywords: telemedicine, sentiment analysis, Named Entity Recognition (NER), topic modeling, social network analysis, COVID-19 AbstrakTelemedicine menjadi fenomena yang semakin relevan dalam sektor kesehatan di Indonesia, terutama dengan munculnya Pandemi COVID-19. Penelitian ini mengkaji analisis teks pemberitaan telemedicine selama pandemi COVID-19 di Indonesia dengan menggunakan analisis sentimen, Named Entity Recognition (NER), Topic Modeling, dan Social Network Analysis (SNA). Tujuan penelitian ini adalah untuk memperoleh pemahaman yang mendalam tentang isu-isu, persepsi masyarakat, jaringan sosial, dan topik-topik yang terkait dengan pemanfaatan telemedicine dalam menghadapi masalah kesehatan di masa pandemi. Penggunaan gabungan empat metode analisis agar dapat menyajikan pemahaman yang komprehensif tentang pemberitaan telemedicine selama pandemi COVID-19 di Indonesia. Hasil penelitian menunjukkan adanya kecenderungan sentimen positif dan netral terhadap telemedicine dan keberadaannya sangat membantu masalah kesehatan di masa Pandemi COVID-19. Selain itu pejabat pemerintah adalah nama yang paling sering muncul dalam pemberitaan telemedicine yang memiliki makna peranan sentral pemerintah dalam masalah kesehatan sangat dibutuhkan. Penelitian ini diharapkan dapat memberikan wawasan berharga bagi para pemangku kepentingan dalam mengoptimalkan pemanfaatan telemedicine, memahami persepsi masyarakat, dan membangun kolaborasi yang efektif dalam penanganan pandemi.Kata Kunci: telemedicine, analisis sentimen, Named Entity Recognition (NER), social network analysis, topic modelling, COVID-19

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“…The community also relates their opinions to their expectations of conditions in Indonesia. Vaccine halal status is also one of the hot topics discussed by the public on social media (Rachman & Pramana, 2020). The number of negative issues about Covid-19 affects people's perceptions of not participating in the Covid-19 vaccination.…”

Section: Introductionmentioning

confidence: 99%

Effect of Health Coaching on the Community's Availability to Vaccinate

Najamuddin,

Wahab,

Amin

et al. 2021

JVNUS

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Background: The number of negative issues regarding COVID-19 affects the public's perception of not participating in the COVID-19 vaccination. Based on a brief interview conducted by the researcher with 20 people in the Matangnga Health Center working area regarding the implementation of vaccination, 17 people expressed their doubts and were not willing to vaccinate. Because in this study, what needs to be changed is the public's perception of the COVID-19 vaccination, the health education method that will be used is the health coaching method. Objective: To determine the effect of providing health coaching on people's willingness to vaccinate in the work area of the Matangnga Health Center, Polewali Mandar Regency. Methods: The research design used was experimental research with a one-group pretest-posttest research design. Population In the study, namely all people over the age of 18 years, amounting to 3,444 people with a sample of 20 respondents, Sampling was done using the multistage sampling technique. The data collection tool used in this study was a questionnaire consisting of 17 statement items. Results: The normality test of the data using the Saphiro Wilk test showed that the data was normally distributed, and then the data was analyzed using the paired t test with the results of the p value of 0.000 <0.05. Conclusion: The results of the analysis of community attitudes towards the implementation of vaccination before and after the provision of health coaching showed that there was an effect of providing health coaching on the community's willingness to vaccinate in the work area of the Matangnga Health Center, Polewali Mandar Regency. Suggestion: It is hoped that this health coaching intervention can be developed further and can be used as a method for providing health education.

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Analisis Sentimen Opini Terhadap Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Naïve Bayes Dan Decision Tree

Cited by 4 publications

References 2 publications

The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm

The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm

Analisis Teks Pemberitaan Telemedicine di Indonesia: Pendekatan Sentimen, NER, Topic Modeling, dan Social Network dalam Memahami Isu dan Persepsi

Effect of Health Coaching on the Community's Availability to Vaccinate

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