Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Decision Tree dalam Analisis Sentimen Ulasan Aplikasi DANA pada Google Play Store

Abstract

The rapid growth of digital wallet applications such as DANA has raised concerns regarding the quality of services provided to users. One effective approach to evaluate service quality is through sentiment analysis of user reviews on the Google Play Store platform. However, the large volume of available review data makes manual analysis inefficient. This study aims to identify the most optimal classification algorithm for sentiment analysis of DANA application reviews by comparing the performance of the K-Nearest Neighbor (K-NN) and Decision Tree algorithms. The dataset consists of 723 reviews obtained from Kaggle, divided into 578 training data and 145 testing data. The reviews are classified into three sentiment categories: positive, negative, and neutral. The research process includes data collection, filtering, preprocessing (case folding, tokenizing, stopword removal, and token length filtering), TF-IDF weighting, implementation of classification algorithms, and evaluation using a Confusion Matrix. The results show that the K-NN algorithm achieves an accuracy of 53.10%, precision of 90.32%, and recall of 41.79%, while the Decision Tree algorithm yields a higher recall but lower accuracy and precision. Based on the comparison of these evaluation metrics, the K-NN algorithm is recommended as the more optimal method, as it provides a better balance between prediction accuracy and error rate compared to the Decision Tree.