Penerapan Algoritma Random Forest untuk Menganalisis Ulasan Aplikasi Spotify pada Google Play - Insany, Gina Purnama; Kharisma, Ivana Lucia; Rosmawati, Rosmawati (Jurnal Pendidikan Informatika (EDUMATIC), 2024)
The development of internet technology and mobile devices has played an important role in transforming the music industry and driving the emergence of music streaming services, such as Spotify. This research aims to understand Spotify users' preferences and expectations through sentiment analysis of user reviews. The review data was retrieved from Google Play Store using web scraping technique, including rivew and rating of 1000 Indonesian and 1000 English reviews. Random Forest algorithm was used to model the sentiment of the reviews, with the analysis process including data collection, labeling, preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, oversampling, modeling, and evaluation with confusion matrix. Algorithm testing was conducted using 70% training data and 30% test data. The classification evaluation results of the Random Forest algorithm showed a model accuracy of 88.4% for Indonesian reviews and 93.6% for English reviews. These findings show that the Random Forest algorithm is effective for sentiment analysis in a multilingual context and can help app developers improve service quality based on user sentiment. This research was also deployed using Streamlit, enabling access and usage by users for fast and interactive sentiment analysis.