Analisa Optimasi Grid Search pada Algoritma Random Forest dan Decision Tree untuk Klasifikasi Stunting

Stunting is a serious problem that is of global concern because of its significant impact on the health and growth of children under five. This condition occurs due to long-term malnutrition. In Indonesia, nutritional problems are still common, including stunting which affects children's growth and development. In this regard, data mining has an important role in facing this challenge. Therefore, the aim of this research is to optimize stunting classification using Decision Tree and Random Forest algorithms optimized with Grid Search. This optimization was carried out to increase the accuracy of the two algorithms and identify algorithms that are superior in determining stunting. The dataset used consists of 10,000 toddler data with important attributes related to health conditions. The analysis results show that the initial Decision Tree model has an accuracy of 70.2%. After optimization using Grid Search, the accuracy of the Decision Tree model increased significantly to 82.8%. Meanwhile, the initial Random Forest model achieved an accuracy of 77.9%, and after optimization with Grid Search, its accuracy increased even higher compared to Decision Tree, namely 84.1%. This increase reflects the effectiveness of optimization in increasing the model's ability to classify stunting more accurately. This research provides important insights into the effectiveness of both algorithms in identifying stunting and emphasizes the importance of optimization to improve classification accuracy, which can support appropriate interventions for the well-being of future generations.