| dc.description.abstract | Telemedicine is a solution for providing health services by medical personnel to
provide long-distance medical services by applying data and communication
technology. such as providing diagnostic data, treating, preventing disease and
injury, researching and evaluating, and ongoing learning about the importance of
health. The variety of health services influences potential users to obtain
information and determine which telemedicine services to use. Reviews from
users aim to be a solution for companies to classify the speech of telemedicine
service users based on predetermined aspects. This research utilizes telemedicine
user reviews which are divided into sentiment levels, namely positive, negative
and neutral. However, sentiment analysis alone is not enough to carry out
classification, further development needs to be carried out so that the system
provides more accurate and reliable analysis results, therefore 5000 review
datasets from the Halodoc telemedicine service were used. The prepossessing, TFIDF
and TF-RF weighting stages were carried out as a comparison in order to find
out which word weighting process is more effective to implement using the KNearest
Neighbor method. The research was carried out by applying the confusion
matrix method so that the final accuracy value was obtained based on 4 aspects. In
TF-IDF the highest accuracy used K=11 was 81% and in TF-RF the highest
accuracy used K=7 was 78%. | en_US |
