Analisis Adaptive dan Collaborative Governance Terhadap Determinan Sosial Kesehatan Menggunakan Metode Agglomerative pada Kasus Stunting di Jawa Barat Pada Tahun 2024
Keywords:
Stunting, Social Determinants of Health, Agglomerative Hierarchical Clustering, Adaptive Governance, Collaborative Governance, Data Mining, West JavaAbstract
This study aims to analyze stunting vulnerability across 27 regencies and cities in West Java Province in 2024 using the Agglomerative Hierarchical Clustering method. The variables used include key social determinants of health such as access to safe drinking water, access to proper sanitation, exclusive breastfeeding coverage, iron supplementation for pregnant women (TTD), population density, cases of undernutrition among children, and poverty rates. Data were obtained from Open Data Jabar and the West Java Statistics Bureau (BPS), and processed through the Knowledge Discovery in Database (KDD) stages, including data selection, cleaning, normalization, and clustering modeling. The results show that three optimal clusters were formed with a Silhouette score slightly above 0.50. Cluster 1 represents regions with high stunting vulnerability (five cities), cluster 0 includes regions with moderate vulnerability (21 regencies/cities), while cluster 2 consisting solely of Pangandaran Regency represents low vulnerability. These findings highlight significant spatial variations in the social determinants of health across regions. The study underscores the importance of adaptive governance to support evidence-based policymaking, as well as the role of collaborative governance to ensure coordinated multi-sectoral interventions that align with each region’s unique characteristics.
Penelitian ini bertujuan untuk menganalisis kerentanan stunting di 27 kabupaten/kota di Provinsi Jawa Barat pada tahun 2024 dengan menggunakan metode Agglomerative Hierarchical Clustering. Variabel yang digunakan mencakup determinan sosial kesehatan seperti akses air minum dan sanitasi layak, cakupan ASI eksklusif, konsumsi TTD pada ibu hamil, kepadatan penduduk, gizi balita, dan tingkat kemiskinan. Data diperoleh dari Open Data Jabar dan BPS Jawa Barat, kemudian diolah melalui tahapan KDD meliputi seleksi, pembersihan, normalisasi, hingga pemodelan klaster. Hasil analisis menunjukkan bahwa tiga klaster optimal terbentuk dengan nilai Silhouette sedikit di atas 0,50. Cluster 1 dikategorikan sebagai wilayah dengan kerawanan stunting tinggi (5 kota), cluster 0 sebagai wilayah dengan kerawanan sedang (21 kabupaten/kota), dan cluster 2 sebagai wilayah dengan kerawanan rendah yang hanya dihuni Kabupaten Pangandaran. Temuan ini memperlihatkan adanya variasi spasial yang kuat pada determinan sosial kesehatan antarwilayah. Hasil penelitian menegaskan pentingnya pendekatan adaptive governance dalam pengambilan kebijakan berbasis bukti (evidence-based) serta pentingnya collaborative governance antar Organisasi Pemerintahan Daerah untuk memastikan intervensi lebih tepat sasaran dan sesuai karakteristik wilayah.
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Bahrun, M., & Wildan, A. (2024). A Bibliometric Analysis Of Stunting Research In Indonesia: Trends And Policy Implications. Journal Of Health Policy And Management, 9(1), 45–60.
Barat, B. J. (2024). Survei Status Gizi Indonesia (Ssgi) Provinsi Jawa Barat 2024. Https://Jabar.Bps.Go.Id
Habibah, E. N. (2021). Collaborative Governance: Konsep & Praktik Dalam Pengelolaan Bank Sampah.
Han, J., Kamber, M., & Pei, J. (2022). Data Mining: Concepts And Techniques (4th Ed.). Morgan Kaufmann.
Julianti, N., & Widiartha, I. M. (2024). Penentuan Jumlah Klaster Optimal Menggunakan Elbow Method Dan Silhouette Coefficient Pada Data Pengangguran Terbuka Di Indonesia. Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(2), 155–165.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding Groups In Data: An Introduction To Cluster Analysis. Wiley-Interscience.
Kementerian Kesehatan Republik Indonesia. (2024). Laporan Nasional Status Gizi Balita Indonesia Tahun 2024. Kementerian Kesehatan Ri.
Kickbusch, I., & Buckett, K. (2013). Health In All Policies: Seizing Opportunities, Implementing Policies. World Health Organization.
Marmot, M., & Wilkinson, R. G. (2005). Social Determinants Of Health. Oxford University Press.
Nurzaky, A. (2022). Adaptive Governance Dalam Pemerintahan Lokal : Meninjau Tata Kelola Jogjaplan Dalam Upaya Pembangunan Diy.
Prasetyo, R., & Nurdin, A. (2023). Clustering Of Stunting Vulnerability Areas In Indonesia Using K-Means Algorithm. Indonesian Journal Of Data Science, 5(2), 78–90.
Ramadhan, M. A., Heryani, A., Ramdani, A., Barat, J., & Publik, P. (2022). Adaptive Governance Dalam Pelayanan Disabilitas Di. 9, 239–251.
Sari, D., Nurhasanah, F., & Putri, A. (2023). Determinants Of Stunting Among Children Under Five In Indonesia: Socioeconomic And Environmental Perspectives. International Journal Of Public Health Research, 15(2), 101–112.
Syalom, M. A., Khaira, R., & Arsa, D. (2025). Klasterisasi Daerah Rawan Stunting Di Indonesia Menggunakan Data Terbuka. Jurnal Sistem Informasi Dan Sains Data, 4(1), 22–30.
Unicef. (2021). Unicef Data: Monitoring The Situation Of Children And Women - Stunting. Https://Data.Unicef.Org/Topic/Nutrition/Malnutrition/
Wilkinson, R., & Pickett, K. (2009). The Spirit Level: Why More Equal Societies Almost Always Do Better. Penguin Books.
World Health Organization. (2010). A Conceptual Framework For Action On The Social Determinants Of Health. Who.
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