Analisis Tingkat Kemiskinan di Indonesia Dengan Metode DBSCAN
Keywords:
Kemiskinan, DBSCAN, Machine Learning, ClusteringAbstract
Kemiskinan merupakan permasalahan multidimensional yang masih menjadi fokus utama pembangunan di Indonesia. Penelitian ini bertujuan untuk menganalisis tingkat kemiskinan kabupaten/kota di Indonesia menggunakan metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Data dianalisis menggunakan Google Colaboratory dengan parameter DBSCAN berupa nilai epsilon (ε) sebesar 0,5 dan minimum points (MinPts) sebesar 5.Hasil pengujian menunjukkan bahwa metode DBSCAN menghasilkan 2 klaster utama dan 7 data teridentifikasi sebagai noise (cluster −1). Klaster pertama mencakup 68 kabupaten/kota dengan karakteristik tingkat kemiskinan relatif sedang, sedangkan klaster kedua terdiri dari 34 kabupaten/kota dengan tingkat kemiskinan tinggi. Keberadaan data noise menunjukkan wilayah dengan karakteristik kemiskinan yang bersifat ekstrem dan berbeda dari pola umum.Hasil ini membuktikan bahwa DBSCAN mampu mengelompokkan wilayah berdasarkan kepadatan karakteristik kemiskinan serta mengidentifikasi wilayah outlier yang memerlukan perhatian kebijakan khusus.
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Copyright (c) 2025 Maria M Mitan, I Wayan Sudiarsa, Andrianus Koda, Stanisilia D. Wero Koda, Moh M. Azmi Koda (Author)
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