Optimalisasi Random Forest dan Support Vector Machine dengan Hyperparameter GridSearchCV untuk Analisis Sentimen Ulasan PrimaKu
- Titik Misriati Universitas Bina Sarana Informatika, Jakarta, Indonesia
-
Riska Aryanti
*
Universitas Bina Sarana Informatika, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
PrimaKu App has been a pioneer in the field of digital health since 2017. Through this application, parents can regularly and continuously monitor their children’s health and development. PrimaKu also has a formal alliance with the Indonesian Pediatric Association (IDAI) to promote child health in Indonesia. This application can be downloaded through the Google Play Store. Google Play Store has a feature that allows users to review the app before downloading. Sentiment analysis is used to distinguish between positive and negative reviews by users who have provided reviews so that an evaluation of the services provided can be made. This research aims to conduct sentiment analysis of user reviews of the PrimaKu application using Random Forest (RF) and Support Vector Machine (SVM) algorithms with TF-IDF weighting. Optimization was performed using hyperparameters to improve the performance of the Random Forest and SVM algorithms. The data used consisted of the 2,293 most relevant reviews collected from the Google Play Store. The most effective models for the Random Forest and Support Vector Machine were selected by adjusting the hyperparameters using GridSearch CV. The results of this study show that Random Forest has a higher success rate in classifying PrimaKu user review data, with an accuracy of 89%, precision of 88%, recall of 81%, and F1-Score of 85%.
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