Covid-19, a contagious disease, has been classified
as a global pandemic. Indonesia, as one of the ASEAN countries,
has taken various measures to combat the spread of this disease.
One of the government's initiatives to tackle the pandemic is the
PeduliLindungi application, through which the public provides
feedback on government policies. However, analyzing and
comprehending public opinions in a non-subjective manner poses
a challenge in objectively evaluating government services. This
study aims to address this issue by conducting a sentiment analysis
of Covid-19 handling in Indonesia, using a lexicon-based weighting
system that includes SentiStrengthID and InSet. The decision tree
(DT) machine learning algorithm is utilized to evaluate the
polarity results provided by the lexicon. The results indicate that
the sentiment polarity towards Covid-19 handling in Indonesia is
negative based on both SentiStrengthID and InSet weights.
Evaluating machine learning performance with the
SentiStrengthID lexicon, the DT-entropy and DT-gini models
achieved an accuracy of 82% and 83%, respectively. Similarly,
evaluating machine learning performance with the InSet lexicon,
the DT-entropy and DT-gini models achieved an accuracy of 81%
and 82%, respectively.