Analisis Sentimen Aplikasi Liputan6.Com pada Ulasan Pengguna di Google Playstore dengan Menggunakan Algoritma Support Vector Machine (Svm) dan Naïve Bayes - Yayang Tika Robiatush Sholiha; Lubna Asjad Muhda Nabilah; Imron Imron (Saturnus: Jurnal Teknologi dan Sistem Informasi, 2025)
This study aims to evaluate user sentiment toward the Liputan6.com application available on the Google Play Store. In the digital era, user reviews serve as a significant indicator in assessing the quality of an application. However, the inconsistency between rating scores and review content renders manual analysis less objective. To address this issue, a machine learning approach was adopted by comparing two algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). A total of 2,500 reviews were collected through a web scraping process and automatically labeled based on the rating (positive if ≥ 3, negative if < 3). The data preprocessing stages included cleaning, case folding, tokenizing, stopword removal, and token filtering. Subsequently, word weighting was carried out using the TF-IDF method, followed by classification using 10-Fold Cross Validation in RapidMiner. The evaluation results indicate that, in the positive class, NB demonstrated superior precision (89.47%), whereas SVM achieved higher recall (98.94%) and F1-score (90.96%). In the negative class, SVM performed better in terms of precision (66.15%), while NB attained higher recall (65.65%) and F1-score (36.34%). Further evaluation based on AUC and accuracy positioned SVM in the good category (AUC 0.842; accuracy 83.82%), while NB was categorized as fail (AUC 0.505; accuracy 60.87%). Overall, SVM is considered to be more effective than NB.