Implementasi Algoritma Machine Learning Untuk Prediksi Awal Stunting Pada Anak Usia Dini Berdasarkan Tinggi Badan Dan Berat Badan
DOI:
https://doi.org/10.62951/modem.v2i3.130Keywords:
Stunting, K-Nearest Neighbors, Prediction, Data Normalization, Machine Learning AlgorithmAbstract
This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.
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