Kamil, Muhammad Insan (2025) IMPLEMENTING NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFYING TODDLERS’ NUTRITIONAL STATUS (Case Study: Cilandak Community Health Center, West Cilandak Village). Tugas Akhir thesis, Informatics.
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Abstract
Malnutrition and stunting among toddlers remain critical public health challenges in Indonesia, with high prevalence rates reported by the Jakarta Health Office. The current manual classification of nutritional status through anthropometric methods may lead to inaccuracies. This study aims to develop a nutritional status classification system for toddlers using the Naive Bayes Classifier (NBC) to assist nutritionists in making faster and more accurate assessments. The dataset, obtained from the Cilandak Community Health Center in West Cilandak, includes six key features: gender, age, birth weight, birth height, measured weight, and measured height. Data preprocessing techniques such as class weighting, Synthetic Minority Over-sampling Technique (SMOTE), and random oversampling were employed to address class imbalance. The model successfully classified nutritional status into three categories—normal, overnutrition, and undernutrition—with an evaluation accuracy of 86%. However, precision and sensitivity for the undernutrition category require further improvement. This research highlights the importance of preprocessing techniques in enhancing model performance and recommends exploring alternative algorithms to handle complex inter-feature relationships. The developed system is expected to support nutritionists in classifying nutritional status prior to conducting direct anthropometric measurements.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 16 Jul 2025 01:51 |
| Last Modified: | 16 Jul 2025 01:51 |
| URI: | http://eprints.uty.ac.id/id/eprint/18152 |
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