=LDR  00000nam  2200000   4500
=001  INLIS000000000004498
=005  20250724104018
=035  ##$$a 0010-0725000309
=007  ta
=008  250724###########################0#eng##
=020  ##$$a 2503-3379
=082  ##$$a 362.198 92
=084  ##$$a 362.198 92  MUH j
=100   #$$a Muhammad Firmansyah¹
=245  1#$$a Jurnal Keluarga Berencana MACHINE LEARNING FOR CHILD MALNUTRITION PREDICTION IN INDONESIA /$c Muhammad Firmansyah¹, Lalu Kekah Budi Prasetya², Nurul Adi Prawira³
=250  ##$$a Vol. 10 No. 1 Tahun 2025
=260  ##$$a Jakarta :$b KEMENTERIAN KEPENDUDUKAN DAN PEMBANGUNAN KELUARGA/BKKBN,$c 2025
=300  ##$$a 13 hlm : $b - ; $c 29 cm
=700   #$$a Lalu Kekah Budi Prasetya²
=700   #$$a Nurul Adi Prawira³
=520  ##$$a This study utilizes machine learning (ML) methods to predict malnutrition among children in Indonesia. It adopts a family-centered perspective, leveraging large datasets from WHO, UNICEF, and the Global Nutrition Report. A 100,000-strong stratified sample of children aged 0-18 was examined, incorporating a set of anthropometric, socioeconomic, and demographic factors. Preprocessing of data included imputation, normalization, and feature selection. Results revealed a dual burden of malnutrition, with stunning proportion being 25.3% and overweightness proportion being 8.6%, and regional disparities indicating higher proportions in rural areas and provinces such as Aceh and South Kalimantan. Analysis of feature importance identified weight-for-age, parental education, household income, and access to clean water as key predictors of health outcomes. The model had peak performance for children aged 6-10 years. These findings highlight the strength of ML to improve health surveillance, inform targeted nutritional interventions, and enhance evidence-based policymaking. The framework provides actionable insights for enhancing national initiatives, such as BANGGA Kencana, so that family planning efforts are aligned with overall child health targets. Future studies should focus on improving data quality for rural environments, adding environmental and dietary factors into models, and exploring advanced ensemble models for higher generalizability and applicability to policy.
Keywords: Child Malnutrition; Family Planning; Machine Learning; Predictive Modeling.
=856  ##$$a Perpustakaan KEMENDUKBANGGA/BKKBN