Data-Driven Clustering of Stunting Prevention Services for Pregnant Women and Infants Using Fuzzy C-Means

Hanum Zalsabilah Idham; Ayu Safitri; Andi Akram Nur Risal; Dewi Fatmarani Surianto; Firdaus

doi:10.66053/aieds.v1i2.22

Authors

Hanum Zalsabilah Idham Universitas Negeri Makassar
Ayu Safitri Universitas Negeri Makassar
Andi Akram Nur Risal Universitas Gadjah Mada
Dewi Fatmarani Surianto Universitas Negeri Makassar
Firdaus Universitas Negeri Makassar

DOI:

https://doi.org/10.66053/aieds.v1i2.22

Keywords:

Baby, Fuzzy C-Means, Health services, Pregnant women, Stunting

Abstract

Purpose – This study addresses persistently high stunting rates in South Sulawesi, Indonesia, which remain above national targets despite declining trends. We developed a clustering model to overcome limitations of traditional methods in handling complex health data with overlapping characteristics, aiming to identify priority regions requiring targeted interventions.
Methods – Using 2,267 structured records from Satu Data Indonesia covering maternal and child health indicators, we implemented Fuzzy C-Means (FCM) algorithm with systematic preprocessing, optimal cluster determination via Elbow Method, and quality validation using Silhouette Coefficient.
Findings – Analysis revealed three distinct clusters for pregnant women (representing good, moderate, and low service coverage areas) and three corresponding clusters for infants. Validation showed Silhouette values ranging from 0.204 to 0.645, indicating variable cluster separation quality with Cluster 0 pregnant women achieving highest cohesion (0.638) and Cluster 2 infants showing strongest separation (0.645).
Research limitations – Data quality limitations affected cluster cohesion in some areas, particularly Cluster 1 infants (0.204 Silhouette value), constraining generalizability. The FCM approach accommodates real-world data complexity better than rigid clustering methods but requires high-quality input data.
Originality – This research contributes an adaptive framework for evidence-based stunting prevention through sophisticated data-driven segmentation. Findings offer immediate practical value for health policymakers in resource allocation and intervention planning, with potential adaptation to other regional contexts facing similar public health challenges.

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