OPTIMASI PENGELOMPOKAN PROVINSI BERDASARKAN INDIKATOR SDGs MENGGUNAKAN K-MEANS DAN ANALISIS FEATURE IMPORTANCE - Salsabila, Salsabila; Anggraini Susanti, Leni (LPPM Universitas Bina Bangsa, 2025)
This study clusters Indonesian provinces based on the indicators of adequate housing using the K-Means method. The four key indicators analyzed are access to clean water (X1), sanitation (X2), floor area per capita (X3), and building resilience (X4). The K-Means algorithm is applied to group the provinces based on their proximity to centroids calculated from each province's data. The clustering results in four groups with distinct characteristics, each requiring data-driven interventions to improve housing quality. Additionally, feature importance techniques are used to identify the factors most influential in the clustering process. The analysis reveals that building resilience (X4) and floor area per capita (X3) are the most important indicators in the clustering, while access to clean water (X1) is more homogeneous across provinces. Based on these findings, policy recommendations focus on improving building resilience in provinces with lower X4 scores, as well as enhancing access to clean water and sanitation in areas with challenges in X1 and X2. This K-Means and feature importance-based approach can be used to formulate more effective and sustainable policies to achieve the SDGs in Indonesia.