Integration of Individual Data and Family Cards in Optimizing Tax Revenue: a Public Accounting and Predictive Analytics Approach to Regional Development Planning

Sarsiti Sarsiti; Tamam Rosid; Juli Prastyorini; Putri Nilam Aisyah

doi:10.59890/ijfbm.v4i1.176

Authors

Sarsiti Sarsiti University Surakarta
Tamam Rosid Universitas Muhammadiyah Berau
Juli Prastyorini STIAMAK Barunawati Surabaya
Putri Nilam Aisyah Universitas Surakarta

DOI:

https://doi.org/10.59890/ijfbm.v4i1.176

Keywords:

Tax Revenue Optimization, Data Integration, Predictive Analytics, Public Accounting, Regional Development Planning, Family Registration System

Abstract

This study examines the integration of individual population data with family registration records to optimize regional tax revenue in Indonesia. Using a mixed-methods approach across three districts (2021–2024), the findings show that integrated data systems increase tax compliance by 23–31% and expand the tax base by 18–26%. Predictive analytics using machine learning achieved 84% accuracy in identifying potential taxpayers and 78% precision in predicting payment behavior. Data integration also reduced administrative costs by 35–42% and improved revenue forecasting accuracy from 68% to 89%. Despite challenges related to legal frameworks, interoperability, and institutional capacity, integrated data systems enhance tax administration efficiency and support more equitable regional development planning.

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