Analysis Of Opinion Sentiment Towards Electric Vehicle Tax On Social Media X Using The Support Vector Machine Method

Jusli, Dara Taqa Assajidah (2024) Analysis Of Opinion Sentiment Towards Electric Vehicle Tax On Social Media X Using The Support Vector Machine Method. Journal of Computer Networks, Architecture and High Performance Computing.

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Abstract

Electric vehicle tax is increasingly becoming an important issue related to environmental and fiscal policies. Electric vehicles are considered an environmentally friendly solution to reduce greenhouse gas emissions and dependence on fossil fuels. However, public perception of electric vehicle tax is still mixed. This study aims to analyze public sentiment about electric vehicle tax based on data from social media platform X, using the Support Vector Machine (SVM) method. The data used was taken through a crawling technique with a total of 1,014 valid data. The data was then classified into positive and negative classes with a transformer. In this analysis, the data was divided with a ratio of 8:2 between training data and test data. 811 were used as training data and 203 as test data. The research stages involved data preprocessing, sentiment labeling, data separation into training and test data, and weighting using TF-IDF. After that, SVM was applied to classify tweets into positive and negative sentiments. The test results showed that the SVM algorithm had an accuracy of 79%, precision of 85%, recall of 89%, and F1-score of 87%. Based on the results of this research, some people feel unsure about the government's policy regarding electric vehicle tax, because it is considered unfair to the lower middle class. Electric vehicles are considered more expensive than fuel-powered vehicles, so this policy is considered unprofitable.

Jenis Item:	Artikel
Subjects:	000 Generalities > 005 Computer programming, programs, data
Divisions:	Fakultas Sains dan Teknologi > Ilmu Komputer
Pengguna yang mendeposit:	Mrs Siti Masitah
Date Deposited:	23 Jan 2025 03:24
Last Modified:	23 Jan 2025 03:24
URI:	http://repository.uinsu.ac.id/id/eprint/23794

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