Perbandingan Metode Naïve Bayes dan Support Vector Machine untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi Roblox
DOI:
https://doi.org/10.55606/jutiti.v6i1.6569Keywords:
Naïve Bayes, Roblox, Sentiment Analysis, Support Vector Machine, Text MiningAbstract
This study aims to compare the performance of two popular classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM), in analyzing the sentiment of user reviews of the Roblox game application found on the Google Play Store. The data used in this study amounted to 2,000 user reviews collected through web scraping techniques from January to December 2023. The data processing stage began with text pre-processing, including data cleaning, word normalization, tokenization, stopword removal, and stemming. Next, the text data was converted into numeric form using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting method. The dataset was then divided into 80% training data and 20% test data for model training and testing purposes. The performance evaluation of both algorithms was carried out using a Confusion Matrix with an accuracy indicator. The results showed that the Support Vector Machine algorithm performed superiorly compared to Naïve Bayes, with the highest accuracy level reaching 87.20%. This finding indicates that SVM is more effective in handling game review data that has high dimensions and non-standard language variations.
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