Prediction Stunting Analysis Using Random Forest Algorithm and Random Search Optimization

Abstract

Stunting is a nutritional problem experienced by toddlers, characterized by a height below the average. This condition arises due to various factors, one of which is the nutritional issues faced by toddlers. Stunting cases in Indonesia are relatively high, reaching 21.6% in 2022, indicating a significant prevalence of stunting. The identification of stunting is carried out through a data mining approach, deemed more efficient. However, the classification algorithm in data mining often encounters data imbalance, leading to low accuracy and inaccurate prediction results. To address this, the study employs the Random Forest algorithm with optimization using the random search method. The test results demonstrate that Random Forest achieves a relatively high accuracy of 90.7%. After optimization using random search, accuracy further increases to 96.33%. The combination of the algorithm and optimization proves to be highly effective, resulting in a 5.63% increase in accuracy. These findings hold crucial implications in supporting decisions for preventing stunting in toddlers. This research serves as a valuable source of information for the Health sector in identifying and implementing more effective strategies for stunting prevention. The use of the Random Forest algorithm optimized with random search proves to be an efficient solution in addressing data imbalance