Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine
| Main Author: | Fitri, Evita |
|---|---|
| Format: | Article info Data Mining, Metode Naive Bayes; Random Forest; Support Vector Machine application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Jurusan Teknologi Informasi Universitas Semarang
, 2020
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| Subjects: | |
| Online Access: |
https://journals.usm.ac.id/index.php/transformatika/article/view/2317 https://journals.usm.ac.id/index.php/transformatika/article/view/2317/1668 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/327 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/328 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/329 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/330 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/331 https://journals.usm.ac.id/index.php/transformatika/article/downloadSuppFile/2317/332 |
Daftar Isi:
- The review of the users of one application is of great help to development in improving the quality of the application and may be the means for assessments that users feel satisfied or not. The study conducted a sentiment analysis of the Ruangguru application by testing the three classification models such as Naive Bayes, Random Forest and Support Vectors Machine. The study has yielded results that from Random Forest classification model 97,16% by using Cross Validation and an AUC score of 0.996. Then accuracy with the model of Support Vector Machine classification support results in accuracy rate of 96.01% to an AUC value of 0.543 and accuracy in the testing of Naive Bayes classification model was 94,16% of AUC score 0,999. This study shows that an increase in accuracy from previous studies of 7.16% with Random Forest’s final cut as a Random Forest classification model with the best performance.