Aspect-Based Sentiment Analysis of YouTube Comments on CoreTax Using Support Vector Machine and Random Forest

Nur Vadila; Josua Josen A. Limbong; Ratna Juita

doi:10.61628/jsce.v7i3.2722

Nur Vadila Universitas Papua
Josua Josen A. Limbong Universitas Papua
Ratna Juita Universitas Papua

DOI: https://doi.org/10.61628/jsce.v7i3.2722

Keywords: CoreTax, Random Forest, Support Vector Machine, Aspect-based Sentiment Analysis, YouTube Comments

Abstract

The implementation of the CoreTax Administration System (CTAS) by the Directorate General of Taxes has received diverse responses from the public, which are reflected in YouTube comments. This study applies Aspect-Based Sentiment Analysis (ABSA) to identify user opinions regarding CoreTax and compares the performance of Support Vector Machine (SVM) and Random Forest for sentiment classification. Data were collected through web scraping from three Youtube videos, yielding 1.527 valid comments after preprocessing. A rule-based method was used to classify comments into five aspects, namely system, performance, user-friendliness, tax services, and policy. The results indicate that the system aspect was the most frequently discussed (56,12%), while negative sentiment dominated the dataset (59,2%). The highest proportion of negative sentiment was found in the user-friendliness aspect (81,29%), followed by performance (76,44%). In model evaluation, Random Forest achieved better results than SVM, obtaining 0.80 accuracy, 0.84 precision, 0.75 recall, and 0.79 F1-Score. Overall, ABSA provides deeper insights into user perceptions and issues related to CoreTax implementation.

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