Comparison Of Machine Learning Algorithms For Analyzing Sentiments Of Reviews On The Digital Korlantas Polri Application
DOI:
https://doi.org/10.70609/gtech.v8i4.5089Keywords:
Sentiment Analysis, Digital Korlantas Polri Application, Machine LearningAbstract
Korlantas Polri Digital Application is one of the mobile applications that provides ease for the public in extending the driving license. Sentiment analysis of user reviews can help korlantas polri identify public perception of the given service. The study aims to evaluate which of the five machine learning algorithms performed best from Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression in sentiment analysis. The evaluation was done by measuring accuracy, precision, recall and F1 measure. There were 10,000 reviews labelled with linguistic validation, re-processed, and word weighted after data was collected. Synthetic minority over-sampling techniques (SMOTE) are applied before data splitting for training and testing. The evaluation shows that Random Forest and SVM do the best. Random Forest has an accuracy of 90.77%, recall 90.77%, and its highest F1 rating is 90.79%. SVM has the highest precision with 91.14% among other algorithms, which shows the great potential of both of these algorítms in the analysis of sentiment reviews of digital applications Korlantas Polri.
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