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All Journal Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences
Berawi, Khairunisa
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Variable Effect Analysis of Household Characteristics, Family Aware of Nutrition (Kadarzi) and Environmental Sanitation as Risk Factors for Stunting Events (Comparative Study of Agricultural and Non-Agricultural Family in the Regency of South Lampung Regency, 2022) Berawi, Khairunisa; Pramudyawati, Yustika; Kurniawaty, Evi; Bakri, Samsul; Zuraida, Reni
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 4 (2022): Budapest International Research and Critics Institute November
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i4.7125

Abstract

This study aims to analyze the effect of household characteristics, Kadarzi and environmental sanitation as risk factors for stunting in a comparative study of agricultural and non-agricultural families in South Lampung district. This study uses primary data obtained from questionnaires, that are data of family characteristics, Kadarzi and environmental sanitation from 198 household samples and secondary data, that are data of stunting toddlers and agricultural and non-agricultural areas in South Lampung district in 2022. To achieve the purpose of research, the research method used is observational analytic with a cross sectional approach. The research was conducted from July 2022 to September 2022. The results showed that on the household characteristics variable, variables that had a significant effect were family occupation and family income, while variables that had no significant effect were parents’ education, nutritional knowledge, and gender of children. On the Kadarzi variable, variables that had a significant effect were the behavioral variables of Kadarzi and exclusive breastfeeding, while variables that had no significant effect were variables of weight measurement, various foods, iodized salt and nutritional supplements. On the environmental sanitation variable, the variables that have a significant effect are family latrines, sewerage and agricultural and non-agricultural zones, while variables that have no significant effect are variables of clean water sources and trash bins.
Performance Evaluation of Support Vector Machine (SVM) and XGBoost for Predicting Toddlers’ Stunting Status Based on Anthropometric Data Nurjoko, Nurjoko; Syarif, Admi; Lumbanraja, Favorisen R.; Berawi, Khairunisa
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1260

Abstract

Stunting remains a primary global health concern, particularly in developing countries, due to its long-term effects on physical growth, cognitive development, and overall well-being. Despite various public health initiatives, challenges in early detection persist, highlighting the need for accurate, data-driven predictive models to support targeted interventions. This study aims to develop and compare the performance of two machine learning algorithms—SVM and Extreme Gradient Boosting (XGBoost)—for classifying stunting status among children under five, in order to determine the most effective method for early prediction. A quantitative machine learning approach was applied to a dataset comprising 17,498 records derived from Posyandu data in Lampung Province, Indonesia. The analytical pipeline included data preprocessing, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and model evaluation through stratified 10-fold cross-validation. Performance was assessed using accuracy, precision, recall, and F1-score. The XGBoost model demonstrated superior performance with accuracy, precision, recall, and F1-score reaching 0.9979. In comparison, the SVM model produced slightly lower yet still strong results, achieving an accuracy of 0.9949, with similarly consistent performance across other evaluation metrics. These findings indicate that XGBoost more effectively handles high-dimensional, imbalanced data and captures nonlinear patterns in the dataset. XGBoost was identified as the optimal method for stunting classification in this study, outperforming SVM across all evaluation metrics. These results support the integration of boosting-based models into early detection systems for child nutritional assessment. Future studies should incorporate additional environmental and socioeconomic variables and evaluate model applicability in a real-time community health setting.
Co-Authors - Nurjoko Admi Syarif Kurniawaty, Evi Lumbanraja, Favorisen R Pramudyawati, Yustika Reni Zuraida Samsul Bakri