Sentiment Analysis of Indonesian Government's Effort to Overcome the Unemployment Problem during COVID-19 Pandemic
Pandu Maulana, Indra Darmawan Budi, Aris Budi Santoso · 2022 5th International Conference on Information and Communications Technology (ICOIACT) · 2022
The impact of the COVID-19 pandemic has affected the unemployment problem in Indonesia, and it has become one of the public's concerns in the past two years. In August 2020, which was 9.8 million people and August 2021, which was 9.1 million people. Given these conditions, the government needs to make improvements regarding the current unemployment problem. The main objective of this research is to find out how public opinion is regarding the government's efforts to overcome the unemployment problem during the COVID-19 pandemic in Indonesia. Sentiment analysis was carried out on public opinion using Twitter as a data source. To measure the performance of the model, three algorithms are used, namely Naïive Bayes, Decision Tree, and Random Forest. The results of this study indicate that there are positive labels that have 1710 sentiments, and negative labels that have 1553 sentiments. The best algorithm obtained in this study is Random Forest, with an accuracy value of 79%. This study produces 15 features that affect the unemployment problem, the highest positive weight is ‘ignore’, and the influential feature with the highest negative weight is ‘stamp’.