Optimization of Support Vector Machine Algorithm Using Stunting Data Classification

Abstract

Several studies from Indonesia reveal that malnutrition and stunting are still severe concerns to be addressed in the future. The complexity of the problem of stunting or nutritional status requires the responsibility of all parties, including science and technology. The issue of monitoring and data collection related to stunting or the nutritional status of children in Indonesia, especially Medan City, North Sumatra Province, is an essential factor in determining the calculations carried out by each Community Health Center with many attributes. Currently, the Support Vector Machine method is a solution to increase government intervention's effectiveness in classifying malnutrition and stunting. However, the Support Vector Machine algorithm still needs to improve, namely the difficulty of selecting the right and optimal features for the attribute weights, causing a low prediction accuracy. Therefore, researchers aim to optimize the Support Vector Machine Algorithm with Particle Swarm Optimization using Linear, Polynomial, Sigmoid, and Radial Basic Function kernels. The results were obtained from research utilizing nutritional status data, that performance in improving the Support Vector Machine algorithm based on Particle Swarm Optimization using four kernel tests, namely Linear, Polynomial, Sigmoid, and Radial Basic Function obtained different results, not all kernels in this study can improve accuracy well. The best performance is using the Radial Basic Function kernel with an Accuracy value of 78%, Precision of 89%, Recall of 66%, and F1-Score of 72%, so it is feasible for accurate information regarding the classification of nutritional status.