Analisis Sentimen Ulasan Co-Pilot Google Play dengan SVM, Neural Network, dan Decision Tree

Abstract

Sentiment analysis is a technique used to understand user opinions through product or service reviews. The purpose of this research is to compare three classification methods, namely Support Vector Machine (SVM), Neural Network (NN), and Decision Tree (DT) in analyzing the sentiment of users of the Indonesian-language Microsoft Co-Pilot application taken from the Google Play Store. The dataset consists of 20,000 reviews, which first went through preprocessing such as normalization, tokenization, stopwords removal, and stemming. The three methods we used in this study have a Multilayer Perceptron (MLP) architecture with three hidden layers and a ReLU activation function, as well as a dropout regularization technique to avoid overfitting. Model evaluation was conducted using accuracy, precision, recall, and F1-score, with the results showing that NN achieved the highest accuracy of 95.5%, followed by SVM with 95.4% and DT with 92.1%. The advantage of the NN method lies in its ability to recognize more complex patterns in Indonesian, especially in handling informal text and code-mixing. This research contributes to the development of Artificial Intelligence (AI)-based applications by providing insights into the effectiveness of classification methods in Indonesian sentiment analysis, which is important for improving service quality and the development of NLP technology in Indonesia. The practical implications of this research can be used in the development of AI-based applications that are more responsive to user sentiment.