PERBANDINGAN METODE NAIVE BAYESIAN CLASSIFICATION (NBC) DAN NEIGHBOR WEIGHTED K-NEARETS NEIGHBOR (NWKNN) DALAM MENGKLASIFIKASI STATUS GIZI PADA REMAJA

WIJAYA, MUHAMMAD SATRIO and Sazaki, Yoppy and Miraswan, Kanda Januar (2019) PERBANDINGAN METODE NAIVE BAYESIAN CLASSIFICATION (NBC) DAN NEIGHBOR WEIGHTED K-NEARETS NEIGHBOR (NWKNN) DALAM MENGKLASIFIKASI STATUS GIZI PADA REMAJA. Undergraduate thesis, Sriwijaya University.

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Abstract

Body mass index is used as a measuring tool to assess nutritional status in adolescents. Anthropometric measuring instrument becomes a very important role to determine the nutritional status. On the other hand, the field of numerical computation also experiencing very rapid progress in removing algorithms are commonly called data mining. algorithms developed in the field of computing such as Naive Bayesian Classification and Weighted K-Nearest Neighbor Neighbor that will be used in this research .Algoritma Naive Bayesian Classification and Neighbor Weighted K-Nearest Neighbor will be applied in this study to determine the nutritional status of a person using a measuring instrument anthropometric more than two as input variables. This research will be conducted classification nutritional status consisting of very thin, thin, normal and obese at 250 amounts of data that will be divided into training data and test data. Results from this study showed that the method classifying NWKNN better nutritional status in adolescents due to get a 79% accuracy rate while NBC method is only able to obtain an accuracy of 70%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Nutritional Status of Adolescents, Data Mining, Naive Bayesian Classification, Weighted K-Nearest Neighbor Neighbor
Subjects:	T Technology > T Technology (General) > T58.4 Managerial control systems Information technology. Information systems (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 4462 not found.
Date Deposited:	17 Jan 2020 07:21
Last Modified:	17 Jan 2020 07:21
URI:	http://repository.unsri.ac.id/id/eprint/24444

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