Makhlulia, Nabila Alif (2025) Application of categorical boosting and k-nearest neighbor algorithms for classifying nutritional status of toddlers / NABILA ALIF MAKHLULIA</p>. Diploma thesis, Universitas Negeri Malang.
Full text not available from this repository.Abstract
p Wasting is one of the nutritional problems in toddlers characterized by low body weight relative to height. This condition needs to be recognized early so that toddlers can receive appropriate treatment. This study aims to identify the nutritional status of toddlers using the Categorical Boosting (CatBoost) and K-Nearest Neighbor (KNN) algorithms. In this study an oversampling method Adaptive Synthetic (ADASYN) was applied to address the imbalanced class distribution in the dataset. In addition hyperparameter tuning was performed using GridSearch to find the best combination of parameters that yields the highest accuracy. The evaluation results show that the CatBoost algorithm combined with oversampling and hyperparameter tuning achieved an accuracy of 98.81% while the KNN algorithm with oversampling and hyperparameter tuning achieved an accuracy of 90.64%. /p
| Item Type: | Thesis (Diploma) |
|---|---|
| Divisions: | Fakultas Matematika dan IPA (FMIPA) > Departemen Matematika (MAT) > S1 Matematika |
| Depositing User: | library UM |
| Date Deposited: | 18 Jul 2025 04:29 |
| Last Modified: | 09 Sep 2025 03:00 |
| URI: | http://repository.um.ac.id/id/eprint/394325 |
Actions (login required)
 |
View Item |