TY - GEN
T1 - Sentiment Analysis of Indonesian Government's Effort to Overcome the Unemployment Problem during COVID-19 Pandemic
AU - Maulana, Pandu
AU - Budi, Indra
AU - Budi Santoso, Aris
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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'.
AB - 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'.
KW - COVID-19
KW - Government's Efforts
KW - Sentiment Analysis
KW - Twitter
KW - Unemployment Problem
UR - http://www.scopus.com/inward/record.url?scp=85145353223&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT55506.2022.9971853
DO - 10.1109/ICOIACT55506.2022.9971853
M3 - Conference contribution
AN - SCOPUS:85145353223
T3 - ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding
SP - 144
EP - 149
BT - ICOIACT 2022 - 5th International Conference on Information and Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information and Communications Technology, ICOIACT 2022
Y2 - 24 August 2022 through 25 August 2022
ER -
