PEMBANGKITAN POPULASI SINTETIS KEPENDUDUKAN KOTA CIREBON MENGGUNAKAN HIBRIDA CTGAN DAN ITERATIVE PROPORTIONAL FITTING
DOI:
https://doi.org/10.70248/jrsit.v4i1.4471Keywords:
Data Sintetis, CTGAN, Iterative Proportional Fitting, Populasi Sintetis, KependudukanAbstract
Keterbatasan akses terhadap data mikro kependudukan di Indonesia akibat regulasi perlindungan data pribadi menyulitkan analisis level individu, sementara data agregat publik berisiko kehilangan variasi antarindividu dan menimbulkan ecological fallacy. Penelitian ini mengusulkan pipeline pembangkitan populasi sintetis dengan studi kasus Disdukcapil Kota Cirebon yang mengintegrasikan Conditional Tabular Generative Adversarial Network (CTGAN) dengan Iterative Proportional Fitting (IPF). Indonesia Family Life Survey 5 (IFLS5) digunakan sebagai data mikro referensi berskala nasional untuk mempelajari pola hubungan antarvariabel demografis, dengan keterbatasan bahwa distribusinya tidak secara langsung merepresentasikan karakteristik lokal Kota Cirebon. Secara teoritis, struktur hubungan antarvariabel demografis pada data mikro cenderung lebih stabil lintas wilayah dibandingkan distribusi marginalnya, sehingga IFLS5 tetap dapat menjadi benih awal yang valid sebelum disesuaikan melalui constraint agregat Kota Cirebon pada tahap kalibrasi IPF. Dataset IFLS5 sebanyak 89.382 baris hasil penggabungan disaring menjadi 21.871 baris untuk melatih CTGAN, kemudian benih populasi sintetis dikalibrasi menggunakan data agregat Disdukcapil dan diintegerkan melalui metode Truncate-Replicate-Sample (TRS). Model CTGAN terbaik dipilih pada epoch 200 dari enam variasi epoch yang diuji, berdasarkan keseimbangan kemiripan distribusi dan preservasi hubungan antarvariabel. Setelah kalibrasi IPF, populasi sintetis akhir memperoleh rata-rata Jensen-Shannon Similarity (JSS) 0,9856, Mean Absolute Error (MAE) 0,0062, dan MAE matriks Cramer's V 0,0279, sementara seluruh constraint menghasilkan Weighted Absolute Percentage Error (WAPE) di bawah 10%. Hasil tersebut menunjukkan CTGAN dan IPF saling melengkapi dalam membentuk populasi sintetis yang mempertahankan struktur mikro data referensi sekaligus menyesuaikan distribusi agregat wilayah target, dengan tetap mempertimbangkan keterbatasan representasi lokal dan risiko privasi pada kombinasi atribut bergranularitas tinggi.
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