A Multi-Layer Business Analytics Framework for Evaluating DJP Online's Digital Tax Policy

This dataset was compiled to support the analysis of public sentiment and engagement toward the Directorate General of Taxes’ (DJP) digital tax platform, DJP Online and its mobile application M-Pajak. Data were collected through web scraping of user reviews from the Google Play Store (Application ID: id.go.pajak.djp) and include anonymized attributes such as username, review content, rating score (1–5 stars), and posting date. The final dataset contains 1,500 entries reflecting public perceptions of DJP’s digital services.

All data were preprocessed for text cleaning, normalization, and anonymization to remove personal identifiers. The dataset was further used for sentiment classification (positive, neutral, negative) using machine learning models including Multinomial Naïve Bayes, SVM, and IndoBERT, and for ARIMAX-based predictive modeling to evaluate the relationship between sentiment, engagement, and funnel conversion.

This dataset supports quantitative research on e-government service evaluation, digital engagement behavior, and policy effectiveness in Indonesia’s tax administration context.