Estimasi Tax Gap Indonesia: Analisis Diskrepansi PDRB Resmi Berdasarkan Nighttime Lights Berbasis Machine Learning: Estimating Indonesia's Tax Gap: Analysis of Discrepancies in Official GRDP Based on Machine Learning-Based Nighttime Lights

Fachri Husein Harahap

doi:10.57152/malcom.v6i2.2568

Authors

Fachri Husein Harahap Politeknik Keuangan Negara STAN

DOI:

https://doi.org/10.57152/malcom.v6i2.2568

Keywords:

Machine Learning, Nighttime Light, Shadow Economy, Tax Gap, XGBoost

Abstract

Penelitian ini mengusulkan metodologi baru untuk mengestimasi 'True PDRB' dan 'shadow economy' di tingkat provinsi Indonesia menggunakan latent variable framework [7]. Peneliti menggunakan PDRB riil sebagai variabel laten yang diestimasi dari dua pengukuran independen yang tidak sempurna: PDRB resmi BPS, yang dimodelkan memiliki error underreporting sistematis, dan PDRB-geospatial, yang diprediksi dari multi-source proxy data (NTL, NO?, informalitas, konsumsi) menggunakan model machine learning XGBoost. Untuk menghindari penalaran melingkar, model XGBoost dilatih pada target tertimbang adaptif yang menyesuaikan bobot PDRB resmi berdasarkan tingkat informalitas provinsi. Model menunjukkan daya prediksi kuat (R² = 0.8256) pada data uji temporal 2023. Estimasi GMM mengungkap shadow economy agregat nasional sebesar 6,76% dari PDRB resmi, dengan estimasi tax gap nasional Rp 16,17 triliun (7,03% dari potensi). Temuan menunjukkan heterogenitas ekstrem: provinsi berbasis agrikultur seperti Kalimantan Tengah (46,9%) dan Papua Barat (37,8%) memiliki persentase shadow economy tertinggi. Sebaliknya, tax gap nominal terbesar terkonsentrasi di provinsi dengan PDRB sangat besar, yaitu Papua (Rp 34,5 T) dan Jawa Timur (Rp 8,9 T). Temuan ini menghasilkan pemetaan tax gap yang dapat ditindaklanjuti bagi DJP untuk merumuskan strategi ekstensifikasi berbasis data.

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