Analysis of Knowledge and Skills of Cadres in Early Detection of Stunting Toddler Development in Soppeng Regency, Indonesia

Abstract

Background: About 16% of Indonesian toddlers experience neuro developmental and brain disorders ranging from mild to severe, every two days 1,000 babies experience motor development disorders. The prevalence of stunting in South Sulawesi is in the 20th lowest ranking, namely 27.4% based on the provincial level in 2021. Objective: Recognizing the increased knowledge and skill of cadre and mothers and toddlers in early development detection of stunted infants using a Developmental Pre-Screening Questionnaire (DPQ) in the working area of the Panincong Health Center, Soppeng Regency. Method: This study used a quasi-experimental design with a pretest-posttest control group involving 30 cadres and 30 mothers with 15 children in each group selected by systematic random sampling. Data were analyzed using paired t-tests and Independent t-tests. Results: The results showed that there were differences in knowledge (p=0.000) in the intervention group and differences in knowledge levels in the control group (p=0.044). The level of knowledge after treatment was greater in the intervention group (11.8%), while the knowledge after treatment in the control group was only (2.9%). There was a difference in the skills of the cadres (p=0.000) after being given the Android-based DPQ intervention. There was a difference in the knowledge (p=0.000) and skills of toddler mothers (p=0.000) after being given the Android-based DPQ intervention applied by the cadres. Conclusion: Android-based DPQ is effective as an educational medium and as an alternative to cadres and parents in monitoring or early detection of child development using Android-based DPQ.

Fuzzy expert system design for detecting stunting

“…A problem that has an exact vagueness value is solved using Mamdani fuzzy logic because this logic is the most appropriate way to map the problem to the input space and then forward it to the output space and has a continuity value [35]. For Mamdani's fuzzy set theory, regions that have a range indicating the degree of membership are represented by language logic [36]. A set that is strictly defined, meaning that every element in the universe is clearly defined whether each element in it is part of its members or not is called a crisp set [16].…”

“…To calculate the level of accuracy between expert predictions and fuzzy method predictions, calculations were carried out using the mean percentage error (MPE) calculation [36]. In carrying out calculations using MPE for this case, expert prediction data is coded into expert prediction (EP) and prediction data using the fuzzy method is coded into fuzzy prediction (FP).…”