Skripsi

PENGKLASIFIKASIAN TINGKAT OBESITAS MENGGUNAKAN METODE NAIVE BAYES DAN RANDOM FOREST

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Penilaian

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Obesity is a disease of excess body fat that is abnormal in adipose tissue. Obesity in Indonesia has experienced a significant increase, Basic Health Research in 2018 said the population aged 18 years or over of obesity increased from 14.8% to 21.8%. Obesity can cause complication such as heart disease and stroke, which are the leading cause of death in the world. Therefore, it is quite important to predict whether someone is overweight or not so that it can be treated early. In this research, secondary data were used taken from kaggle.com. This data has 17 variables and 2111 data with 7 classifications of obesity levels. Prediction of the classification of obesity levels using the Naïve Bayes and Random Forest methods. In the Random Forest method, 9 trees were built. The results of this research are the accuracy rate of Naïve Bayes of 68.56% and the Random Forest of 84.63%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107003453	T50992	T509922021	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T509922021

Publisher

Inderalaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2021

Collation

xiii, 118 hlm. : ilus. ; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.207

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Ilmu Matematika
Metode Naive Bayes
Random Forest

Specific Detail Info

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Statement of Responsibility

DS

Other version/related

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