Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Status Gizi Stunting Pada Balita

Abstract

Child stunting is a major public health concern in Indonesia. This study uses the Naïve Bayes classification algorithm to assess the nutritional condition of stunted children based on demographic and anthropometric characteristics. The information used comes from the Toddler Weighing Month (Bulan Penimbangan Balita - BPB) in Majalengka Regency. Data type conversion, separating data into training and testing sets, and data normalization are all examples of preprocessing steps. The model's evaluation results reveal an accuracy of 94.65%, with precision and recall for each category of stunted nutritional status. This study makes a substantial contribution to early diagnosis and mitigation of stunting in Indonesia, as well as providing the framework for future development of more powerful predictive models.