Optimalisasi Random Forest dengan Penyelarasan Temporal untuk Identifikasi Faktor Determinan Stunting Nusa Tenggara Barat - Lalu Mutawalli; Mohammad Taufan Asri Zaen; Ahmad Tantoni; Muhammad Fauzi Zulkarnaen (Bulletin of Information Technology (BIT), 2026)
Stunting remains a major public health issue in West Nusa Tenggara, with key challenges including low data integrity caused by reporting discontinuities, spatial heterogeneity, and statistical noise that obscure accurate determinant identification. These issues are particularly evident during the 2023–2024 reporting transition, where inconsistencies distort the relationship between community health worker performance and stunting prevalence. This study applies a Spatio-temporal approach incorporating temporal alignment, normalization, and Random Forest regression. The analysis includes Moderated Regression Analysis using Ordinary Least Squares, R², and Global Moran's I. The objective is to improve accuracy in identifying determinants of stunting reduction and evaluate community health worker effectiveness using integrated data. Results show consistent decline in stunting prevalence during 2018–2024, with significant reductions in Central of Lombok (-42.2%), West Lombok (-39.4%), and Bima City (-47.7%), while increases occurred in Bima Regency (+8.5%) and Mataram City (+33.6%). Model performance improves significantly after data alignment, with R² exceeding 0.70. Feature importance identifies cadre workload ratio and child weighing participation as dominant predictors (p = 0.073), indicating a discovery effect. Spatial analysis yields Global Moran's I of -0.0615, suggesting a dispersed pattern. Overall, integrating temporal data alignment with machine learning and spatial analysis enhances determinant identification and supports data-driven policy for stunting reduction in West Nusa Tenggara.