PENGGUNAAN K-NEAREST NEIGHBOR DENGAN ALGORITMA GENETIKA UNTUK MENGIDENTIFIKASI GANGGUAN GIZI STUNTING BERDASARKAN PENGUKURAN ANTROPOMETRI

CAHYADI, SALSABILA VIRGINIA and Rini, Dian Palupi and Rizqie, M. Qurhanul (2022) PENGGUNAAN K-NEAREST NEIGHBOR DENGAN ALGORITMA GENETIKA UNTUK MENGIDENTIFIKASI GANGGUAN GIZI STUNTING BERDASARKAN PENGUKURAN ANTROPOMETRI. Undergraduate thesis, Sriwijaya University.

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Abstract

Stunting nutrition disorders are a big problem with nutritional disorders in toddlers, where Indonesia has stunting prevention of 29.6%. Stunting nutrition can result in inhibited intelligence development in children and growth is not optimal, and can trigger obesity or degenerative diseases and potentially increase the rate of pain and death. Therefore, solutions are needed for the identification of stunting nutritional disorders in toddlers to find out and detect whether the toddler is identified as stunting or not. This research was developed to produce software with the K-nearest Neighbor method but the weakness of K-nearest Neighbor is that weighting on K-nearest Neighbor has complex k value and computational problems. So that the method that can be used to perfect the K-nearest Neighbor method is a genetic algorithm, in order to provide optimal results. In the identification of stunting nutritional disorders in this study using anthropometric measurements, namely parameters in the form of height, weight, and age. It obtained quite good accuracy results using a confusion matrix of 80%. Keywords: stunting nutrition, K-Nearest Neighbor, Genetic Algorithm, confusion matrix.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Gizi stunting, K-nearest Neighbor, Algoritma Genetika, confusion matrix.
Subjects:	T Technology > T Technology (General) > T10.5-11.9 Communication of technical information > T10.5 General works Information centers
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Salsabila Virginia Cahyadi
Date Deposited:	27 Jul 2022 04:55
Last Modified:	27 Jul 2022 04:55
URI:	http://repository.unsri.ac.id/id/eprint/74961

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