Sistem Deteksi Dini Anemia pada Anak Usia 0-59 Bulan Menggunakan Naïve Bayes dan Optimasi Particle Swarm Optimization
DOI:
https://doi.org/10.30989/teknomatika.v18i1.1574Keywords:
Early Detection, Anemia, Naïve Bayes, Particle Swarm , OptimizationAbstract
Anemia in children aged 0–59 months poses a serious health concern with long-term effects on growth and development. This study aims to develop a web-based early detection system for childhood anemia using a Naïve Bayes algorithm enhanced with Particle Swarm Optimization (PSO). The system uses secondary data from the 2018 Demographic and Health Survey in Nigeria, which includes variables such as age, nutritional status, and medical history. Although the dataset is from Nigeria, the variables are universal and relevant, making the findings applicable for similar model development in other regions.The Naïve Bayes algorithm is employed for classifying anemia levels, while PSO is applied to improve prediction accuracy by optimizing feature weights and tuning model parameters. Results show an increase in accuracy from 92.17% to 95.71% after optimization. This demonstrates PSO’s effectiveness in improving model performance, especially in datasets with imbalanced class distributions.The system is implemented as a user friendly website, allowing quick and accessible anemia detection. This solution is particularly useful in regions with limited healthcare access. The findings indicate that combining Naïve Bayes with PSO can enhance predictive accuracy and support broader efforts to improve child health outcomes.
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