PENGELOMPOKAN BAYI BERAT LAHIR RENDAH BERDASARKAN ANTROPOMETRI MENGGUNAKAN ALGORITME K-MEANS CLUSTERING (Studi Kasus: RSIA Bunda Arif Purwokerto)

Kurniawan, Deni (2018) PENGELOMPOKAN BAYI BERAT LAHIR RENDAH BERDASARKAN ANTROPOMETRI MENGGUNAKAN ALGORITME K-MEANS CLUSTERING (Studi Kasus: RSIA Bunda Arif Purwokerto). Other thesis, STMIK Amikom Purwokerto.

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Abstract

Low Birth Weight infants with birth weight less than 2500 grams regardless of the time of birth. Low birth weight is the weight of a baby who weighed within 1 hour after birth. The World Health Organization (WHO) since 1961 States that all newborn babies whose weight less or equal to 2500 grams called the low birth weight infant (infants of low birth weight). According To The WHO. Statistically, the number of pain and death in neonates in developing countries is high, with the primary cause is associated with low birth weight. The large number of infant mortality due to low birth weight a factor further to be handled seriously, medical record-keeping for that newborn do not only as documents but need to be processed by a digital processing so that the detection of the risk of low birth weight It can be seen from early birth. To make it easier for medical personnel in determining the risk of low birth weight. One method that can be used to handle data with data that is uncertain or bias is a method of clustering. In this research will be used in an algorithm for k-means in the process of with a low birth weight baby grouped with viewing data Anthropometry. From the testing that was done by the author, then the algorithm for k-means clustering have accuracy in low birth weight babies are grouped based on distance proximity between variables and have similarities with test data, then It could be inferred that an algorithm for k-means clustering was able to perform well in a grouping or infant low birth weight and can be used to assist medical personnel in the view and gives the next action against a group of babies that they low birth weight.

Item Type: Thesis (Other)
Additional Information: Dosen Pembimbing: Irafan Santiko, M.Kom.
Uncontrolled Keywords: Low Birth Weight , K-means, clustering, Data mining
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: UPT Perpustakaan Pusat Universitas Amikom Purwokerto
Date Deposited: 10 Jun 2021 04:46
Last Modified: 10 Jun 2021 04:46
URI: http://eprints.amikompurwokerto.ac.id/id/eprint/986

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