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All Journal Information Technology Education Journal Artificial Intelligence in Educational Decision Sciences
Hanum Zalsabilah Idham
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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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
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.22

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.
Analisis Komponen Antecedents E-Learning, Kesiapan Digital, dan Perilaku Penggunaan Terhadap Kinerja E-Learning Irwansyah Suwahyu; Muh. Agung Sidiq; Muh. Reyhansyah Syahrir; Hanum Zalsabilah Idham
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Berkembangnya teknologi dan internet pada dunia pendidikan membuat penggunaan E-Learning dijadikan sebagai alternatif pembelajaran di berbagai lembaga pendidikan.Penelitian ini bertujuan untuk mengkaji pengaruh faktor-faktor antecedents E-Learning, kesiapan digital, dan perilaku pengguna dalam konteks pengembangan sistem E-Learning. Penelitian ini menggunakan pendekatan kuantitatif dengan metodepenelitian deskriptif.Populasi yang digunakan pada penelitian ini sebanyak 41 orang. Pengumpulan data dilakukan dengan teknik penyebaran kuesioner menggunakan platform Google form. Analisis data dilakukan menggunakan SkalaLikert dengan tujuan untuk memberikan skor dalam bentuk skala pada setiap pernyataan dalam kuesioner. Hasil Penelitian ini menunjukkan bahwa menerapkan e-learning memiliki efek positif yang signifikan. Perlu dilakukan evaluasi berkala terhadap sistem E-Learning untuk meningkatkan kualitas berdasarkan umpan balik dari pengguna.
Co-Authors Andi Akram Nur Risal Ayu Safitri Dewi Fatmarani Surianto Firdaus Irwansyah Suwahyu Muh. Agung Sidiq Muh. Reyhansyah Syahrir