Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana - Diah Fitriani; Afril Efan Pajri; Aprillia Dwi Ardianti (Universitas Budi Darma, 2026)
The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research