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Adinda Febby Nuraini
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Perbandingan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine Pada Analisis Sentimen Twitter Mengenai Opini Masyarakat Terhadap Penghapusan Tenaga Honorer Akhmad Miftahusalam; Adinda Febby Nuraini; Awalia Agustina Khoirunisa; Hasih Pratiwi
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.032 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1410

Abstract

Employees are an important asset in agencies, who plan and act for every activity of an organization, as well as in government agencies. Although employees are important in government agencies, it is not easy to get employees due to the difficulty of approval from the Ministry of State Apparatus Utilization and Bureaucratic Reform. This is what triggers the emergence of temporary or contract workers. The goal is to meet the needs of vacant employees. However, after serving for decades, the government announced it would eliminate the appointment of temporary employees. This news certainly raises the pros and cons in society. To see people's opinion on this issue, the Twitter platform can be used as a data source. Many users are actively writing information in the form of tweets related to the removal of temporary workers. There were 2690 tweets in a week that discussed honorary workers. With the Random Forest method, an accuracy of 66,67% is obtained.
Analisis Sentimen Pelaksanaan Vaksinasi Covid-19 secara Massal pada Media Sosial Twitter Adinda Febby Nuraini; Rosma Dian Pertiwi; Muhammad Zidni Subarkah; Kiki Ferawati
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (369.085 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1564

Abstract

The corona virus or Covid-19 attacks almost all countries in the world, as well as Indonesia. The Indonesian government has implemented several policies to deal with the spread of the Covid-19 in the community, namely by mass vaccination. The implementation of mass vaccination has become trending on Twitter. This study aims to analyze the sentiment of mass vaccination in Indonesia using a comparison between Naïve Bayes, Random Forest, and Support Vector Machine (SVM) methods. The results showed that SVM classification method has F1-Score Weighted Average higher than other methods, which was 84%. In addition, it can be concluded that most of the community is pro against the implementation of mass vaccines. So, SVM method can be used by the government to classify public sentiment towards the next mass vaccination and basis for the government to maintain this mass vaccination program as an effort to prevent the spread of Covid-19.
Co-Authors Akhmad Miftahusalam Awalia Agustina Khoirunisa hasih pratiwi Kiki Ferawati Muhammad Zidni Subarkah Rosma Dian Pertiwi