Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Ulasan Kompas.id pada Data Tidak Seimbang - Muhammad Ardana; Rini Mayasari; Iqbal Maulana (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
The rapid advancement of digital technology and the increasing use of mobile devices have driven the widespread adoption of digital news applications, including Kompas.id. User reviews on the Google Play Store represent an important data source for understanding user satisfaction and emerging issues; however, the large volume of reviews makes manual analysis inefficient. Therefore, this study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying Kompas.id user reviews into positive, neutral, and negative sentiments. The research employs the Knowledge Discovery in Databases (KDD) framework, which includes web scraping, text preprocessing, lexicon-based sentiment labeling, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification and evaluation stages. The dataset consists of 1,023 cleaned reviews after data preprocessing. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that Naïve Bayes achieves an accuracy of 72%, while SVM outperforms it with an accuracy of 80%, reflecting its stronger ability to handle high-dimensional and sparse textual feature spaces. Word cloud visualization reveals that positive sentiments are mainly associated with content quality, whereas negative sentiments are dominated by subscription-related issues and technical problems. Based on these findings, SVM is recommended as a more effective algorithm for sentiment analysis of digital news application reviews.