Implementasi Particle Swarm Optimization (PSO) Pada Analysis Sentiment Review Aplikasi Halodoc Menggunakan Algoritma Naïve Bayes
Abstract
Health is very important for humans, if you experience
symptoms or feel pain, it is appropriate for us to have a health check
and go to a hospital or clinic, but if it is not possible to leave the
house, an online health consultation application is considered to be
helpful. But before you can use and take advantage of these
applications, it is necessary to know reviews from consumers based on
positive opinions and negative opinions. This study applies the Naive
Bayes algorithm to perform text classification and selects the particle
swarm optimazation selection feature to support the increased accuracy
obtained. Classification evaluation and validation are performed using
confusion matrix and ROC curves. The results showed an increase in
accuracy previously 88.50% and AUC 0.535, increased to 90.50% and AUC
0.525. It can be concluded that the selection of the particle swarm
optimazation feature has succeeded in increasing the accuracy.
Keywords: selection features, naïve bayes, particle swarm optimization.
Keterangan: Jurnal Teknologi Informasi 7 (1), 17-23, 2021 /7/1/
Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), Universitas Respati Indonesia
Link Jurnal : http://ejournal.urindo.ac.id/index.php/TI/article/view/1330
