Comparison of Decision Tree and Random Forest Performance for Sentiment Analysis of Public Service App Reviews

Perbandingan Kinerja Decision Tree dan Forest Performance untuk Analisis Sentimen Ulasan Aplikasi Layanan Publik

Abstract

The Info BMKG application is a government-developed digital public service platform designed to provide real-time weather, seismic, and climate information to Indonesian citizens. The substantial volume of user reviews accumulated on the Google Play Store holds significant potential as a service evaluation resource; however, the limitations of manual review processes necessitate an efficient computational approach. This study proposes a machine learning-based sentiment analysis framework to classify user reviews of the Info BMKG application, while systematically comparing the performance of two algorithms   Decision Tree and Random Forest   using a dataset of 10,000 reviews collected via web scraping. The data underwent text preprocessing, rating-based sentiment labeling, and TF-IDF feature extraction, followed by evaluation using accuracy, precision, recall, F1-score, cross-validation, and computational time metrics. Experimental results demonstrate that Random Forest achieved 81% accuracy with a 77% F1-score, outperforming Decision Tree which attained 78% accuracy and 75% F1-score. In terms of efficiency, Decision Tree exhibited faster testing time (0.114 seconds) compared to Random Forest (0.201 seconds), while Random Forest proved more efficient in training time (7.347 seconds versus 12.421 seconds). These findings confirm that Random Forest represents the more optimal algorithm for sentiment classification tasks involving public service application user reviews.