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Analisis Kematangan Implementasi Big Data dalam Kebijakan Pengawasan Perpajakan (Deskriptif Kualitatif: Implementasi Big Data dalam Kebijakan Pengawasan Perpajakan di Direktorat Jenderal Pajak)

Rifky Bagas Nugrahanto, Dr. Mardhani Riasetiawan, SE Ak, M.T.; Dr. Dewi Haryani Susilastuti, M.Sc.

2025 | Tesis | S2 Mag.Studi Kebijakan

Dalam era digital, big data berpotensi meningkatkan efektivitas dan efisiensi pengelolaan administrasi perpajakan, khususnya dalam pengawasan dan kepatuhan wajib pajak. Direktorat Jenderal Pajak (DJP) mulai mengadopsi big data untuk mengoptimalkan pengawasan dan mencegah pelanggaran pajak, meskipun masih menghadapi tantangan seperti kesiapan infrastruktur, kompetensi SDM, dan kebijakan tata kelola data.

Penelitian ini menganalisis tingkat kematangan implementasi big data dalam kebijakan pengawasan perpajakan di DJP, mengevaluasi tantangan yang ada, serta menyusun rekomendasi strategis. Dengan metode studi kasus kualitatif, data dikumpulkan melalui survei, wawancara mendalam terhadap 15 informan, dan analisis dokumen kebijakan. Evaluasi dilakukan berdasarkan kesiapan infrastruktur, SDM, tata kelola data, serta strategi dan kepemimpinan, dengan analisis korelasi Spearman dan Kendall Tau.

Hasil penelitian menunjukkan bahwa implementasi big data di DJP masih pada tingkat kematangan menengah. Infrastruktur cukup memadai, tetapi perlu peningkatan dalam pemrosesan data real-time dan analitik prediktif. Kompetensi SDM masih terbatas, sehingga perlu pelatihan intensif. Data governance masih terfragmentasi antarunit, menghambat integrasi dan kolaborasi. Selain itu, resistensi terhadap perubahan menghambat adopsi teknologi baru, sehingga diperlukan manajemen perubahan yang lebih efektif.

Kesimpulannya, big data berpotensi besar meningkatkan efisiensi administrasi perpajakan, tetapi keberhasilannya bergantung pada kesiapan teknologi, SDM, tata kelola data, dan strategi manajemen perubahan. Implementasi rekomendasi yang diberikan dapat mempercepat transformasi digital DJP dan meningkatkan akuntabilitas perpajakan di Indonesia.

In the digital era, big data has the potential to enhance the effectiveness and efficiency of tax administration management, particularly in tax supervision and taxpayer compliance. The Directorate General of Taxes (DJP) has begun adopting big data to optimize supervision and prevent tax violations, although challenges remain, such as infrastructure readiness, human resource (HR) competence, and data governance policies.

This study analyzes the maturity level of big data implementation in tax supervision policies at DJP, evaluates existing challenges, and provides strategic recommendations. Using a qualitative case study method, data was collected through surveys, in-depth interviews with 15 informants, and policy document analysis. The evaluation was conducted based on infrastructure readiness, HR competence, data governance, strategy, and leadership, with correlation analysis using Spearman and Kendall Tau methods.

The findings indicate that big data implementation at DJP is still at a moderate maturity level. While the infrastructure is relatively adequate, improvements are needed in real-time data processing and predictive analytics. HR competence remains limited, requiring intensive training. Data governance is still fragmented across units, hindering integration and collaboration. Additionally, resistance to change slows the adoption of new technologies, necessitating a more effective change management approach.

In conclusion, big data has significant potential to improve tax administration efficiency, but its success depends on technological readiness, HR competence, data governance, and change management strategies. Implementing the recommended strategies can accelerate DJP's digital transformation and enhance tax accountability in Indonesia.

Kata Kunci : Big Data, Pengawasan Perpajakan, Kematangan Teknologi, Tata Kelola Data, Direktorat Jenderal Pajak.

  1. S2-2025-512162-abstract.pdf  
  2. S2-2025-512162-bibliography.pdf  
  3. S2-2025-512162-tableofcontent.pdf  
  4. S2-2025-512162-title.pdf