Open AccessPerformance of the K-Nearest Neighbors Method on Identification of Maize Plant Nutrients
Author(s) -
Bain Khusnul Khotimah
Publication year - 2022
Publication title -
jurnal infotel/jurnal infotel
Language(s) - English
Resource type - Journals
eISSN - 2460-0997
pISSN - 2085-3688
DOI - 10.20895/infotel.v14i1.735
Subject(s) - closeness , k nearest neighbors algorithm , value (mathematics) , nutrient , identification (biology) , mathematics , minkowski space , commodity , statistics , soil science , computer science , artificial intelligence , environmental science , chemistry , botany , mathematical analysis , geometry , organic chemistry , biology , economics , market economy
Maize is one kind of commodity consumption in domestic as well as export that has high economic value. However, the low productivity is caused by the main factor, namely the decreased level of soil fertility, so that it has the same effect on crop yields. These problems require the application of technology with the K-Nearest Neighbor (KNN) method. The method of study is based on 17 signs of nutrient deficiencies with Minkowski distance calculation process, calculation of deficiency of soil nutrients based on the value of K determined. The test results of the research use K = 75 to get an accuracy of 92.40. Comparative analysis of the K-nearest neighbor (K-NN) and NB methods by looking for the closeness between the criteria for new cases and old case criteria based on the criteria for the closest cases. The results showed that the K-Nearest Neighbor (K-NN) Algorithm had a better accuracy value than NB.