PENERAPAN CENTER DISPLACEMENT FUZZY K-MEANS UNTUK PEMETAAN KEMISKINAN DI PULAU JAWA
DOI:
https://doi.org/10.51878/knowledge.v5i3.6857Keywords:
Kemiskinan, Center Displacement Fuzzy K-Means, Tipologi wilayahAbstract
Poverty in Java Island remains a complex structural challenge despite a downward trend, with significant disparities between districts/cities. This study aims to map regional typologies based on poverty indicators, thus carrying out quantitative research, with the help of the Center Displacement Fuzzy K-Means (CDFKM) algorithm. This method was chosen because it is able to handle overlapping cluster membership with better computational efficiency than conventional Fuzzy K-Means. Secondary data were sourced from BPS (2024) for 119 districts/cities located in six provinces of Java Island. These include poverty levels, education, health, access to clean water, housing density, and household expenditure patterns. The validity of the clustering results was evaluated using the Silhouette Coefficient. The results indicate an optimal configuration of two clusters. The first cluster reflects urban areas with relatively low poverty but facing vulnerabilities in housing density, food expenditure burden, and limited health insurance coverage. The second cluster represents rural areas with higher poverty levels, limited access to clean water, and poorer environmental health conditions. These findings suggest the importance of data-driven approaches in policy formulation, with interventions tailored to the specific vulnerability profiles of each region in Java.
ABSTRAK
Kemiskinan di Pulau Jawa masih menjadi tantangan struktural yang kompleks meskipun ada tren penurunan dengan disparitas antar kabupaten/kota yang signifikan. Tujuan dari penelitian ini adalah memetakan tipologi wilayah berdasarkan indikator kemiskinan melalui pendekatan kuantitatif menggunakan algoritma Center Displacement Fuzzy K-Means (CDFKM). Metode tersebut dipilih karena mampu menangani tumpang tindih keanggotaan klaster dengan efisiensi komputasi yang lebih baik dibandingkan Fuzzy K-Means konvensional. Data sekunder bersumber dari BPS (2024) pada 119 kabupaten/kota yang terletak di enam provinsi Pulau Jawa. Variabel dalam penelitian ini meliputi tingkat kemiskinan, pendidikan, kesehatan, akses air bersih, kepadatan hunian, serta pola pengeluaran rumah tangga. Validitas dari klaster hasil pengelompokkan dievaluasi menggunakan Koefisien Silhouette. Hasil penelitian menunjukkan konfigurasi optimal pada dua klaster. Klaster pertama mencerminkan wilayah urban dengan kemiskinan relatif rendah namun menghadapi kerentanan pada kepadatan hunian, beban pengeluaran pangan, dan keterbatasan jaminan kesehatan. Klaster kedua merepresentasikan wilayah rural dengan tingkat kemiskinan lebih tinggi, akses air bersih yang terbatas, serta beban kesehatan lingkungan yang lebih buruk. Temuan ini menegaskan pentingnya pendekatan berbasis data dalam perumusan kebijakan, dengan intervensi yang disesuaikan terhadap profil kerentanan spesifik tiap wilayah di Pulau Jawa.
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