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Nutritional problems among children under five remain a public health concern in Indonesia, including in the working area of Semitau Public Health Center, Kapuas Hulu Regency. Nutritional status is an important indicator in assessing child growth and development. Nutritional disorders such as undernutrition, severe undernutrition, and stunting may have long-term impacts on health, cognitive development, and productivity. Therefore, regular monitoring of nutritional status is necessary to determine the nutritional profile of children under five in the Semitau Public Health Center area. Objective: This study aimed to describe the nutritional status of children under five based on Weight-for-Age (W/A), Height-for-Age (H/A), and Weight-for-Height (W/H) indicators, as well as to describe the characteristics of mothers and children. Methods: This study used a descriptive method with a retrospective approach. Data were obtained from the recap of child weighing results in March 2025 in the working area of Semitau Public Health Center. The study population consisted of 395 children aged 0–59 months, with 351 children meeting the inclusion criteria selected using total sampling technique. Data were analyzed univariately to obtain the frequency distribution of nutritional status. Results: Based on W/A, 77% of children had normal nutritional status, 13.5% were undernourished, 6.1% were severely undernourished, and 1.0% were overweight. Based on H/A, 62.1% were normal, 20.8% were stunted, 11.5% were severely stunted, and 1.8% were tall. Based on W/H, 76.4% had normal nutritional status, 7.9% were undernourished, 2.0% were severely undernourished, 3.1% were overweight, and 2.6% were obese. Conclusion: The majority of children under five in the Semitau Public Health Center area had normal nutritional status. However, the stunting prevalence of 32.3% remains high according to WHO classification. Strengthening nutritional monitoring through integrated health posts and providing parental education on balanced nutrition are recommended.

Child developmental delay is a public health issue that needs to be identified early to prevent long-term impacts on children’s quality of life. In Rantau Prapat Sub-district, cases are still found among toddlers with undernutrition, incomplete immunizations, and suboptimal developmental stimulation, which may pose risks of growth and developmental delays. This study aims to apply the Naive Bayes method in identifying child developmental delays based on health data collected through medical records and questionnaires. The research method includes data collection, pre-processing (cleaning, transformation, and normalization), classification using the Naive Bayes algorithm, and model validation with the k-fold cross-validation technique. The results showed that out of 150 toddler data samples, 30.7% experienced developmental delays, with the dominant influencing factors being nutritional status and immunization completeness. The Naive Bayes algorithm achieved an accuracy rate of 87.3% with a precision of 84.1%, recall of 85.7%, and F1-score of 84.9%. These findings demonstrate that Naive Bayes can be used as a decision support system in the early identification process of child developmental delays. Therefore, the results of this study are expected to assist healthcare workers, particularly midwives, in improving the quality of early detection and delivering more targeted interventions for children in the Rantau Prapat area.

Non-communicable diseases (NCDs) are one of the biggest health challenges globally. Based on data from the World Health Organization or WHO, data shows that of the 56 million deaths that occurred in the world in 2021, there were 38 million or almost three quarters. Based on data from the Semarang City Health Service, the gender of those receiving health services for Hypertension Sufferers in 2022 shows that the female gender is 161,877 (56%) which is greater than the male gender which is 129,033 (44%), and in the working area of ​​the Gunungpati Community Health Center it is in the fifth highest position. Hypertension sufferers. Nutritional status is also influenced by a good diet and is also influenced by age, especially in women of childbearing age less than 40 years because they have a greater potential for experiencing hormonal imbalances in the body. The respondents in this research were WUS in the Sadeng Gunung Pati sub-district, Semarang city, totaling 101 respondents. The aim of this study was to determine the relationship between nutritional status and the incidence of hypertension in suburban age women (WUS) in the Sadeng Gunung Pati sub-district, Semarang City. This research method uses a cross sectional approach which was carried out in the Sadeng Gunung Pati sub-district. Data collection was carried out from March to May 2024. The results showed that the correlation between nutritional status and the incidence of hypertension in women of childbearing age (WUS) in Sadeng subdistrict was carried out using the Chi Square test, obtained with a P value = 0.0001, OR = 5.15 , CI = (2.16 – 12.26), where the majority of respondents with hypertension were 57.4% (aged 15-49 years) with a BMI classification in the thin category of 18.5. The conclusion of this study is that there is a relationship between nutritional status and the incidence of women of suburban age (WUS) in the Gunung Pati sub-district, Semarang city.

In 2018, Basic Health Research (Riskesdas) data showed that the prevalence of wasting in children under five in Indonesia was 10.2%, while in the age group under two years (baduta) it was higher, namely 11.7%. Nutrition Status Monitoring Data for 2017 also shows that the prevalence of wasting in under-fives is higher, which is 12.8% when compared to the toddler group, which is 9.5% (Ministry of Health RI, 2018). Based on the WHO classification, wasting in the underdog group in Indonesia is categorized as a serious public health problem, with a prevalence in the range of 10-14%. Malnutrition that occurs in 1,000 HPK, besides being at risk of inhibiting physical growth (failure to thrive) and being susceptible to disease (metabolic disorders: DM. Hypertension, obesity), also inhibits cognitive development (motor cognitive impairment) which will affect the level of intelligence and productivity of children in the future.    

Infant health can be known one of them through the assessment of nutritional status. In general, Body Mass Index (BMI) has been used as a method for measuring the nutritional status of children. If there are two children who have same body weight and height, they may have different nutritional status. Whenever this occurs, the use of BMI for measuring the nutritional status shall be deemed less accurate. The anthropometry will be vital in measuring the nutritional statuss. The guidelines for determining the nutritional status Anthropometry parameters are selected and recommended which includes an assessment of the age, weight, body length or height. This research aims to build a model of C4.5 adaboost so it can recognize patterns and be able to classify the nutritional status of children into five classes: normal, fat, very fat, thin and very thin. The variables used in this classification is Gender, Age (Months), Weight (kg) Height (cm). C4.5 (decision tree) Method has a good performance in dealing with the classification of nutritional status but the C4.5 has a weakness in the class imbalance. Adaboost isone ofboosting methods that could reduce imbalances class by giving weight to the level of classification error which may alter the distribution of data. Experiments carried out by applying the adaboost method C4.5 to obtain optimal results and a good degree of accuracy. The experimental results obtained from C4.5 method show that accuracy is 89.53%, the error rate is 10.47%, while the results of C4.5 with adaboost show 90.23% accuracy and 9.77% error rate. It can be concluded in the classification of nutritional status of children with C4.5 and adaboost proven method to solve problems of class imbalance and improve the high accuracy and can reduce the level of classification error.