Stunting masih menjadi masalah kesehatan masyarakat di Indonesia dengan prevalensi 19,8% pada tahun 2024. Penetapan prioritas intervensi umumnya dilakukan pada tingkat populasi sehingga belum mampu mengakomodasi perbedaan profil risiko individu. Penelitian ini bertujuan mengembangkan kerangka penetapan prioritas intervensi penurunan stunting berbasis profil risiko individu menggunakan pendekatan machine learning. Penelitian menggunakan desain potong lintang dengan data sekunder Survei Kesehatan Indonesia (SKI) 2023 dan Survei Status Gizi Indonesia (SSGI) 2024 pada 381.108 balita usia 0–59 bulan. Analisis dilakukan pada tiga subgrup usia (0–5 bulan, 6–23 bulan, dan 24–59 bulan) menggunakan algoritma Logistic Regression, Random Forest, dan XGBoost. Kinerja model dievaluasi berdasarkan ROC-AUC, precision, recall, dan F1-score. Hasil menunjukkan bahwa Logistic Regression merupakan model terbaik pada usia 0–5 bulan (ROC-AUC=0,764), sedangkan XGBoost Tuned menjadi model terbaik pada usia 6–23 bulan (ROC-AUC=0,663) dan 24–59 bulan (ROC-AUC=0,661). Faktor prediktor dominan serta prioritas intervensi berbeda pada setiap subgrup usia. Implementasi model melalui dashboard menghasilkan rekomendasi prioritas intervensi berdasarkan perubahan probabilitas risiko setelah faktor intervensi dimodifikasi pada profil individu. Pendekatan ini menghasilkan kerangka penetapan prioritas intervensi berbasis risiko individu yang berpotensi mendukung pengambilan keputusan penurunan stunting secara lebih presisi, preventif, dan berbasis data.

Stunting remains a major public health problem in Indonesia, with a prevalence of 19.8% in 2024. Current intervention prioritization is generally conducted at the population level and does not adequately account for individual risk profiles. This study aimed to develop an individual risk-based framework for prioritizing stunting interventions using machine learning approaches. A cross-sectional study was conducted using secondary data from the 2023 Indonesia Health Survey (SKI) and the 2024 Indonesia Nutritional Status Survey (SSGI), involving 381,108 children aged 0–59 months. Analyses were performed across three age subgroups (0–5 months, 6–23 months, and 24–59 months) using Logistic Regression, Random Forest, and XGBoost algorithms. Model performance was evaluated using ROC-AUC, precision, recall, and F1-score. The results showed that Logistic Regression achieved the best performance in the 0–5 months subgroup (ROC-AUC = 0.764), while XGBoost Tuned performed best in the 6–23 months (ROC-AUC = 0.663) and 24–59 months (ROC-AUC = 0.661) subgroups. The dominant predictor factors and intervention priorities differ for each age subgroup. Implementing the model through a dashboard generates intervention priority recommendations based on changes in risk probability after intervention factors are adjusted for an individual's profile. This approach creates a risk-based intervention priority framework that could support more precise, preventive, and data-driven decisions for reducing stunting.

Penelitian ini bertujuan mengidentifikasi faktor risiko dan membangun model prediksi diabetes berbasis machine learning menggunakan data SKI 2023. Sampel terdiri dari individu usia ≥15 tahun. Analisis dilakukan melalui eksplorasi bivariat dan evaluasi sembilan algoritma prediktif. Hasil menunjukkan bahwa faktor usia ≥45 tahun, jenis kelamin, pendidikan, obesitas sentral, hipertensi, pola makan berisiko, dan aktivitas fisik berhubungan signifikan dengan diabetes. Algoritma AdaBoost memberikan performa terbaik (AUC 0,991). Penelitian ini menghasilkan mockup web "Diabetes Risk Assessment" yang berpotensi digunakan untuk skrining mandiri dan pencegahan dini di masyarakat.

This study aimed to identify risk factors and develop a diabetes risk prediction model using machine learning algorithms based on the 2023 Indonesian Health Survey (SKI). The sample included individuals aged ≥15 years who met the inclusion criteria. The analysis involved bivariate exploration and evaluation of nine machine learning algorithms. Results indicated that age ≥45 years, sex, education level, central obesity indicators (BMI, waist circumference, WHtR), hypertension, risky dietary patterns, and physical inactivity were significantly associated with diabetes. Among all models, AdaBoost achieved the highest predictive performance (AUC 0.991). The study also produced a web-based mockup system, “Diabetes Risk Assessment,” offering potential for self-screening and community-level early prevention strategies.