Analisis Sentimen Kendaraan Listrik Pada Twitter Menggunakan Metode Long Short Term Memory - Dian Agus Prawinata; Ani Dijah Rahajoe; I Gede Susrama Mas Diyasa (SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi, 2024)
In facing the increasing awareness of environmental impact, electric vehicles have become a primary focus in the global automotive industry. With the advancement of technology and the growing need for eco-friendly solutions, the evaluation of public sentiment towards electric vehicles becomes highly relevant. This research aims to analyze opinions expressed on Twitter regarding the use of electric vehicles using the Long Short Term Memory (LSTM) classification method. Utilizing a dataset of 30,000 entries, this study applies the LSTM algorithm to classify sentiment in tweets. Four different scenarios are tested, involving combinations of Continuous Bag of Words (CBOW) and Skip-Gram feature extraction methods, as well as data split percentages of 80:20 and 70:30. The research results demonstrate high accuracy levels across all scenarios, ranging from 85.16% to 85.9%. These findings indicate the effectiveness of sentiment analysis in gauging public perspectives on the use of electric vehicles. This study makes a significant contribution to understanding public sentiment related to electric vehicles based on Twitter data while highlighting the application of sentiment analysis techniques in the context of electric vehicle usage.