IRMA SURIANI TAMBUNAN, NPM 2009100080 (2024) ANALISIS DATA MINING CLUSTERING PADA BALITA YANG PENDERITA GIZI BURUK DI PUSKESMAS SIGAMBAL MENGGUNAKAN METODE K-MEANS. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Toddlers with malnutrition are a serious health problem, requiring special attention to ensure their growth and development runs optimally. This research aims to identify and group toddlers based on the severity of their malnutrition conditions, using the K-Means method within the Knowledge Discovery in Database (KDD) framework. Through the KDD stage, toddler data is collected, processed and analyzed to produce clusters that reflect various levels of severity of malnutrition. The clustering results of 108 toddler samples show a division into three different clusters. Cluster C1 includes 42 toddlers with the most severe malnutrition and the widest variety of conditions. Cluster C2 includes 34 toddlers with moderate malnutrition and a more moderate variety of conditions. Meanwhile, Cluster C3 includes 32 toddlers with the mildest conditions of malnutrition and the smallest variation in conditions. This division helps in identifying toddlers who require immediate attention and intervention. Evaluation of clustering results is carried out using scatter plots and box plots, both of which provide a clear visualization of the distribution and variability of data within each cluster. The scatter plot shows a clear spread of data within each cluster. These two evaluation methods are consistent in describing data distribution and strengthening clustering findings, providing a comprehensive and detailed picture of the malnutrition conditions of the toddlers studied. Keywords : Knowledge Discovery in Database (KDD); Data Mining; Metode K Means; Clustering; Malnutrition
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Knowledge Discovery in Database (KDD); Data Mining; Metode K Means; Clustering; Malnutrition |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 05 Sep 2024 08:34 |
| Last Modified: | 05 Sep 2024 08:34 |
| URI: | http://repository.ulb.ac.id/id/eprint/1026 |
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