Prawinata, Dian Agus (2024) Analisis Sentimen Kendaraan Listrik Pada Twitter Menggunakan Metode Long Short Term Memory. Undergraduate thesis, Universitas Pembangunan Nasional Veteran Jawa Timur.
|
Text (Cover)
19081010074_Cover.pdf Download (1MB) | Preview |
|
|
Text (Bab 1)
19081010074_Bab1.pdf Download (156kB) | Preview |
|
![[img]](http://repository.upnjatim.ac.id/style/images/fileicons/text.png) |
Text (Bab 2)
19081010074_Bab2.pdf Restricted to Registered users only until 22 January 2027. Download (313kB) |
|
|
Text (Bab 3)
19081010074_Bab3.pdf Restricted to Registered users only until 22 January 2027. Download (5MB) |
|
|
Text (Bab 4)
19081010074_Bab4.pdf Restricted to Registered users only until 22 January 2027. Download (2MB) |
|
|
Text (Bab 5)
19081010074_Bab5.pdf Download (115kB) | Preview |
|
|
Text (Daftar Pustaka)
19081010074_DaftarPustaka.pdf Download (112kB) | Preview |
Abstract
In the era of increasing awareness of environmental impact, electric vehicles have become a primary focus in the global automotive industry. With the advancement of technology and growing demand for eco-friendly solutions, the evaluation of public sentiment towards electric vehicles has become 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. A comprehensive analysis was conducted on Twitter user sentiments towards electric vehicles to measure public perception and sentiment polarity. This study utilized a dataset comprising 30,000 entries and involved the application of the LSTM algorithm to classify tweet sentiments, followed by an in-depth analysis of the classification results. Four different scenarios were tested, including various combinations of feature extraction methods and different data separation percentages, with the goal of assessing the model's performance. The research results indicated high accuracy levels across all scenarios, ranging from 85.16% to 85.9%. These findings signify the effectiveness of sentiment analysis in measuring public views on the use of electric vehicles. This study makes a significant contribution to understanding public sentiment regarding electric vehicles based on Twitter data and demonstrates the application of sentiment analysis techniques in the analysis of electric vehicle usage.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||||||||
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Dian Agus Prawinata | ||||||||||||
| Date Deposited: | 22 Jan 2024 06:49 | ||||||||||||
| Last Modified: | 29 Jan 2024 03:53 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/20413 |
Actions (login required)
 |
View Item |