<![CDATA[Data quality regarding the nutritional status of children under five is crucial for developing strategies to address nutritional issues. This study aims to develop indicators and assess the quality of anthropometric data from community-based nutrition surveillance using the EPPGBM application in Magelang Regency. The research employed an observational design with a quantitative approach. Data quality indicators were defined based on expert consensus using the Delphi method. These indicators were also used to construct an anthropometric data quality index (IKDA). The WPS Office spreadsheet was utilized to assess data quality and perform IKDA calculations. Nine data quality indicators were identified, categorized into four domains: representation, completeness, accuracy, and external consistency. Evaluation of the EPPGBM data revealed that indicators for representation and completeness were categorized as good quality. In contrast, within the accuracy domain, only the z-score accuracy indicator met the “good” standard, while the digit preference indicator showed poor quality. Specifically, digit preference accounted for 24.2% of weight measurements and 62.8% of height measurements, with clustering around digits 0 and 5. In the external consistency domain, the stunting prevalence from the EPPGBM results was lower than the 2022 SSGI results. The IKDA score for the data was 85.8. Overall, the evaluation identified that the EPPGBM data quality in Magelang Regency demonstrated strong representation and completeness but exhibited limitations in accuracy and external consistency. To improve data accuracy, relevant stakeholders should implement targeted interventions, including capacity-building through training of cadres, standardization of measurement procedures and instruments, and reinforcement of supervisory mechanisms.]]>
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