Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat

Bakhtiyar Hadi Prakoso; Ervina Rachmawati; Demiawan Rachmatta Putro Mudiono; Veronika Vestine; Gandu Eko Julianto Suyoso

doi:10.24002/jbi.v14i01.7105

Authors

Bakhtiyar Hadi Prakoso Politeknik Negeri Jember
Ervina Rachmawati Politeknik Negeri Jember
Demiawan Rachmatta Putro Mudiono Politeknik Negeri Jember
Veronika Vestine Politeknik Negeri Jember
Gandu Eko Julianto Suyoso Politeknik Negeri Jember

DOI:

https://doi.org/10.24002/jbi.v14i01.7105

Keywords:

data mining, K-Means, klasterisasi, Angka Kematian Ibu, Angka Kematian Bayi, prevalensi stunting, data mining, K-Means, clustering, Maternal Mortality Rate, Infant Mortality Rate, the prevalence of stunting

Abstract

One of the fundamental principles followed by the Jember Health Office for decision-making is data. Data plays a crucial role in the decision-making process. Raw data is more difficult to interpret and needs to be analyzed. Clustering is one of the techniques used for analysis. This study discusses using K-Means to cluster Public Health Center data based on AKI, AKB, and stunting prevalence. The data is processed by reducing dimensions and normalizing them. The clustering process is performed using the K-Means method, where the maximum k-value is obtained by calculating WCSS. The clustering process results in three clusters of Public Health Centers in the Jember Regency. These clusters can serve as a reference for the Jember Health Office to formulate family health and community nutrition quality policies.
Keywords: data mining, K-Means, clustering, Maternal Mortality Rate, Infant Mortality Rate, the prevalence of stunting

Salah satu dasar pengambilan kebijakan oleh Dinas Kesehatan Jember adalah data. Data memiliki peran dalam proses pengambilan keputusan. Data mentah yang didapatkan lebih sulit untuk diinterpretasikan sehingga diperlukan analisis terhadap data tesebut. Salah satu analisis yang dapat digunakan adalah teknik klasterisasi. Pada
penelitian ini akan dibahas penggunaan K-Means untuk klasterisasi data puskesmas berdasarkan AKI, AKB, dan prevalensi stunting. Data diproses dengan melakukan reduksi dimensi dan normalisasi. Proses klasterisasi dilakukan dengan metode K-Means dimana nilai k maksimal diperoleh dengan menghitung WCSS. Adapun hasil proses klasterisasi didapatkan tiga kelompok klaster puskesmas yang terdapat di Kabupaten Jember. Hasil klasterisasi dapat digunakan sebagai referensi Dinas Kesehatan Jember dalam mengambil kebijakan terkait kualitas kesehatan keluarga dan gizi masyarakat
Kata Kunci: data mining, K-Means, klasterisasi, Angka Kematian Ibu, Angka Kematian Bayi, prevalensi stunting

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