Accuracy in Sentiment Analysis of the by.U Application Using Naïve Bayes and SMOTE Techniques

Athhar Hafizha Luthfi; Ahmad Faqih; Gifthera Dwilestari

doi:10.59934/jaiea.v4i2.737

Authors

Athhar Hafizha Luthfi Teknik Informatika, STMIK IKMI Cirebon
Ahmad Faqih Teknik Informatika, STMIK IKMI Cirebon
Gifthera Dwilestari Sistem Informasi, STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.737

Keywords:

User Sentiment, Naïve Bayes, SMOTE, Sentiment Analysis, by.U

Abstract

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.

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