Hyperparameter Tuning for Optimizing Stunting Classification with KNN, SVM, and Naïve Bayes Algorithms

The purpose of this study is to illuminate and compare the performance of three classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), in classifying stunting data. Using evaluation measures such as accuracy, precision, recall, and F1 score, the performance of each algorithm is measured before and after hyperparameter adjustment. The experimental results show that SVM provides a strong balance between precision and recall before hyperparameter adjustment, KNN excels in recall, and NB achieves the highest precision. After hyperparameter adjustment, all models show improved performance, with SVM achieving the best accuracy and F1 score. While NB remains highly precise and reduces false positives, KNN continues to win the recall. The results show that hyperparameter adjustment is critical to optimizing algorithm performance and that algorithms should be selected according to specific research objectives to maximize detection accuracy and balance recall and precision.