Mining Public Sentiment on Digital Tax System: A Twitter-Based Case Study of Coretax DJP
Kania Alma Tiara, Muhammad Rafi’ Ar-Robbani, Dian Kurnianingrum, Fouqil Rais, RM Hasbi Pratama Arya Agung · 2025
This study examines public sentiment toward Coretax DJP, Indonesia’s newly implemented digital tax administration platform launched in December 2024. As part of the country's digital transformation efforts, Coretax aims to streamline taxpayer services, including registration, reporting, and payment. However, public sentiment can reveal gaps between intended service improvements and actual user experiences. Using Twitter-based sentiment analysis, the research analyzes 15,527 Indonesian-language tweets containing the keyword "Coretax," collected via the Twitter API and processed using Python on Google colab. The analysis applies a five-point sentiment classification (1–5 stars), along with techniques such as tokenization, stopword removal, stemming, volatility detection, outlier identification, and word frequency analysis. Results show that negative sentiment dominates— particularly on weekdays like Thursday—due to login failures, password issues, and other system errors. Frequently used keywords such as "lapor" and "sila," while neutral, often appear in negative contexts, reflecting dissatisfaction. Visual tools including heatmaps and word clouds further support these findings. This research offers recommendations for improving future digital services, including avoiding midweek system updates, conducting pre-launch usability testing, and implementing real-time sentiment monitoring to enhance user responsiveness and satisfaction.