Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Sains Student Research
Al-Fahri, Fajar
Unknown Affiliation
Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sentiment Analysis of Coretax Tax Application Users Using IndoBERT and Web Scraping on the X (Twitter) Platform: Case Study on Indonesian Taxpayer Digital Service Feedback Syabil, Muhammad Al Faris; Al-Fahri, Fajar
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 1 (2026): Februari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i1.7258

Abstract

This study analyzes public sentiment toward the Coretax tax system based on user opinions posted on the X (Twitter) platform. The objective is to assess how the public perceives the system’s stability, accessibility, and performance during periods of high usage. A quantitative text-based approach was applied using Natural Language Processing (NLP) techniques. Data were collected through web scraping of tweets containing Coretax-related keywords and processed through six preprocessing stages: case folding, cleaning, tokenizing, normalization, stopword removal, and stemming. Sentiment classification was conducted using the IndoBERT model mdhugol/indonesia-bert-sentiment-classification, which categorized tweets into positive, negative, and neutral classes. The results show that 181 tweets expressed positive sentiment, 171 negative sentiment, and 29 neutral sentiment. Negative sentiment predominantly relates to system errors and login difficulties, whereas positive sentiment commonly appears when the system functions normally. These findings demonstrate that system instability remains the primary factor influencing negative perceptions of Coretax and provide useful insights for improving the reliability of digital tax services.
Co-Authors Syabil, Muhammad Al Faris