Publication Details
Abstract
This research aims to analyze the implementation of the Regional Revenue Management System (Simpatda) application in optimizing regional tax collection in Hulu Sungai Utara Regency. This issue is important in line with the demands of digital transformation of regional government in improving the effectiveness, transparency, and accountability of Local Own Revenue management. The research uses a qualitative approach with a case study design to deeply understand the dynamics of digital policy implementation, interactions between actors, and taxpayer experiences in utilizing the system. Data collection techniques were carried out through semi-structured in-depth interviews, limited participatory observation, and documentation. Research participants consisted of apparatus from the Regional Revenue Agency, Simpatda system operators, organizational leaders, and taxpayers who use digital services. The data were analyzed using the Miles and Huberman interactive model through data reduction, display, and conclusion drawing. The research results indicate that the implementation of Simpatda has driven improvements in service efficiency, transparency, and accuracy of regional tax collection data. The main themes found include organizational and resource readiness, taxpayer acceptance and digital literacy, improvement in technology-based service quality, strengthening of supervision and accountability, and the impact on optimizing regional revenue. However, the optimization of tax collection has not been fully achieved due to existing obstacles in apparatus capacity, system integration, and public digital literacy. This research contributes to enriching public administration studies, particularly on digital governance integration and policy implementation in regional tax management. Practical implications emphasize the importance of strengthening organizational capacity, digital socialization, and information system integration. Future research is recommended to conduct comparative studies between regions and combine quantitative approaches to strengthen the generalization of findings.