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PREDIKSI KEJAHATAN DENGAN MODEL GRAFIK, DETEKSI ANOMALI, DAN PEMBELAJARAN MULTI-MODAL
Abstract
Kejahatan tetap menjadi tantangan yang kompleks dan terus-menerus yang memengaruhi keamanan dan stabilitas masyarakat di Indonesia. Analisis kejahatan yang efektif, yang penting untuk memahami pola dan merumuskan kebijakan keamanan publik yang responsif, sering kali terhambat oleh sifat data kejahatan yang luas dan beragam. Makalah ini mengusulkan pendekatan baru yang memanfaatkan Pembelajaran Multi-Modal Berbasis Grafik yang terintegrasi dengan Deteksi Anomali untuk meningkatkan prediksi tingkat kejahatan dan menginformasikan pengembangan kebijakan keamanan publik di Indonesia. Kami mengintegrasikan kumpulan data heterogen termasuk data sosio-demografis, ekonomi, lingkungan, dan sentimen publik yang berasal dari Twitter. Alur kerja praproses yang komprehensif dikembangkan, yang mencakup pembersihan data, rekayasa fitur, dan normalisasi, diikuti oleh konstruksi grafik spasio-temporal di mana simpul mewakili kombinasi provinsi-tahun dan tepi menangkap kedekatan geografis dan suksesi temporal. Model Jaringan Konvolusional Grafik (GCN) digunakan untuk mempelajari hubungan yang kompleks dalam struktur grafik multi-modal ini. Sementara evaluasi awal menghasilkan Root Mean Squared Error (RMSE) Uji sekitar 306-314 pada skala asli, yang menunjukkan ruang untuk peningkatan presisi prediktif, utilitas inti model terletak pada kemampuannya untuk mengidentifikasi penyimpangan yang signifikan.
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DOI: http://dx.doi.org/10.26798/jiko.v9i3.2162
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