Analisis Tren dan Klasterisasi Spasial Kemiskinan di Provinsi Jawa Tengah Menggunakan Algoritma Louvain
Abstract
This study aims to project the 2025 poverty levels in Central Java Province and group districts/cities based on their patterncharacteristics to support the formulation of more adaptive and targeted poverty alleviation policies. A descriptive quantitative approach was employed using secondary data on the percentage of poor populations from Statistics Indonesia (BPS) for the 2020–2024 period. The forecasting tools applied include Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Trend Analysis to project poverty rates, followed by cluster analysis using the Louvain algorithm assisted by Principal Component Analysis (PCA) via Orange Data Mining software. The forecasting results indicate that most regions in Central Java are projected to experience a decrease in poverty levels in 2025, except for Wonogiri Regency, which is predicted to increase slightly due to local economic structural factors. Meanwhile, the cluster analysis successfully identified six distinct regional groups with poverty characteristics ranging from low to high categories. In conclusion, the integration of trend forecasting and spatial community detection methods provides a comprehensive overview of the spatial-temporal patterns of poverty in Central Java, serving as a strategic foundation for local governments to design sustainable intervention programs.
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