<![CDATA[Stunting is caused by a lack of nutrients or sickness, and stunted children may have impaired immune systems, increased mortality rates, and are more prone to endure long-term developmental abnormalities. Stunting prevalence in Indonesia remains concerningly high by the end of 2024. Through the use of integrated health posts, or pos pelayanan terpandu (Posyandu), and the technology-based website iPosyandu, attempts are being made to lower the prevalence of stunting. Using toddler data from iPosyandu, this study proposes a Long-Short Term Memory (LSTM) model for predicting stunting based on WHO standards, categorizing children as tall, normal, stunted, or severly stunted. By using a genetic algorithm (GA) for learning rate hyperparameter tuning, the LSTM model is significantly improved. Five generations, each with five populations, were used for the GA-based optimization, which explored learning rates ranging from 5.23E-04 to 8.83E-03. The results show that 7.82E-03 was the optimal learning rate, producing the greatest accuracy of 91.10%, indicating that this range improves the performance of LSTM models.]]>
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