Mengungkap Heterogenitas Stunting pada Anak: Pendekatan Machine Learning untuk Intervensi yang Tepat Sasaran di Sambas, Indonesia

Heru Pramono Hadi, Fadlil Chandra Pratama, Farrikh Al Zami, Yupie Kusumawati, Suharnawi Suharnawi, Ayu Ashari, Henry Bastian, Alfiena Nisa Belladiena

Abstract


Stunting di Kabupaten Sambas, Indonesia, merupakan tantangan kesehatan masyarakat yang kritis dengan implikasi sosio-ekonomi yang signifikan. Penelitian ini bertujuan untuk mengidentifikasi profil kasus stunting secara mendalam melalui pendekatan unsupervised machine learning guna membongkar heterogenitas pada faktor-faktor yang memengaruhinya. Analisis ini menggunakan data kesehatan cross-sectional dari 599 anak (usia 0-11 bulan) di Kabupaten Sambas, yang mencakup data demografis, sosio-ekonomi, dan akses kesehatan dengan dimensionalitas tinggi. Kami menerapkan metodologi yang sistematis, dimulai dengan pra-pemrosesan data ekstensif, reduksi dimensi menggunakan t-distributed Stochastic Neighbor Embedding (t-SNE), dan dilanjutkan dengan segmentasi populasi menggunakan clustering K-Means. Jumlah klaster optimal (k) ditentukan menggunakan metode Elbow. Analisis berhasil mengidentifikasi empat klaster (k=4) yang berbeda secara signifikan. Klaster dengan prevalensi stunting terendah secara konsisten menunjukkan tingkat pendidikan orang tua yang lebih tinggi dan akses terbaik terhadap informasi kesehatan yang disediakan oleh bidan. Sebaliknya, klaster dengan prevalensi stunting tertinggi berkorelasi dengan pendapatan keluarga terendah dan tingkat pendidikan orang tua yang rendah. Temuan kunci yang paling menonjol adalah adanya klaster berpendapatan tertinggi namun tetap memiliki tingkat stunting yang tinggi; analisis lebih lanjut menunjukkan klaster ini memiliki akses terburuk terhadap informasi kesehatan esensial. Penelitian ini menyimpulkan bahwa tingkat pendidikan orang tua dan akses terhadap informasi kesehatan yang dimediasi oleh bidan merupakan prediktor stunting yang lebih kuat dibandingkan status ekonomi semata. Temuan ini membantah asumsi bahwa intervensi ekonomi saja cukup. Hasil penelitian menyediakan basis bukti yang kuat untuk perancangan kebijakan kesehatan publik yang lebih tertarget dan tidak monolitik, dengan memprioritaskan program edukasi dan penguatan peran tenaga kesehatan di lapangan.

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