Khariono, Heri (2022) PENERAPAN ALGORITMA KMEANS CLUSTERING DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI STATUS STUNTING PADA BALITA. Undergraduate thesis, UPN VETERAN JAWA TIMUR.
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Abstract
Stunting is mostly found in developing countries such as Indonesia, in Indonesia the prevalence of stunting has increased from 35.6% in 2019 to 38.9% in 2020. Stunting is a chronic and chronic nutritional status problem that can affect the growth of toddlers. This problem occurs during growth and development since the beginning of growth. The incidence of stunting can be caused by several factors such as poor parenting and poor nutritional intake. At the posyandu in the Sukorame Village area, the grouping of stunting data is still done manually and using Microsoft Excel. So it is necessary to have a system for classifying and classifying stunting data so that it can be carried out more quickly and precisely. This system was built using the PHP and MySQL programming languages by applying Kmeans Clustering to find out the distribution of stunting case data and K-Nearest Neighbor for the stunting data classification process. stunted (very short), stunted (short) or normal. Based on the trials and evaluations that have been carried out by the Kmeans algorithm with the silhouette coefficient method, a value of 0.968779938 is obtained that the cluster used is included in the good and strong category, while the K-Nearest Neighbor algorithm based on the classification results on the data testing the highest accuracy value is found at K = 3 of 85.42%.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Heri Khariono | ||||||||||||
| Date Deposited: | 08 Jun 2022 06:36 | ||||||||||||
| Last Modified: | 08 Jun 2022 06:36 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/6909 |
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