Analisis Sentimen Pengguna pada Aplikasi Tokopedia Menggunakan Algoritma Convolutional Neural Network - Maskhuri, Alip; Hardiani, Tikaridha (Building of Informatics, Technology and Science (BITS), 2025)
The Covid-19 pandemic in 2020 accelerated digital transformation across various sectors, including e-commerce. Tokopedia, Indonesia's largest e-commerce platforms, has experienced significant dynamics in user reviews that can impact its reputation. This study aims to analyze the sentiment of Tokopedia user reviews collected from the Google Play Store and the social media platform X using the Convolutional Neural Network (CNN) algorithm. The research is motivated by the increasing competition in the e-commerce industry, requiring companies to understand consumer sentiment to improve their services. The methodology includes data collection through text mining, data preprocessing, automatic labeling using the Pre-Trained IndoBERT model, and splitting the dataset into training, validation, and testing sets. A total of 15,751 reviews were sentiment with 8,885 classified as negative, 3,860 as neutral, and 3,006 as positive. The CNN algorithm was applied to classify these reviews, and the results showed that the model achieved an accuracy of 83%. The model performed best in recognizing negative sentiment but struggled to distinguish between neutral and positive sentiments due to data imbalance. This study recommends collecting more data to achieve a balanced class distribution and exploring pre-trained models such as IndoBERT or IndoNLU to enhance sentiment analysis accuracy.