Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine
| Autor: | Silfia Andini, Nur Arminarahmah, GS Achmad Daengs, Sara Surya, Muhammad Afdhal |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 2394:012011 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/2394/1/012011 |
| Popis: | Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community’s interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated. |
| Databáze: | OpenAIRE |
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