PENGKLASIFIKASIAN TINGKAT OBESITAS MENGGUNAKAN METODE NAIVE BAYES DAN RANDOM FOREST

S, ABU BAKAR and Resti, Yulia and Yahdin, Sugandi (2021) PENGKLASIFIKASIAN TINGKAT OBESITAS MENGGUNAKAN METODE NAIVE BAYES DAN RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

	Text RAMA_44201_08011281722061.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_44201_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (8MB) | Request a copy
Preview	Text RAMA_44201_08011281722061_0019077302_0027075803_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_44201_08011281722061_0019077302_0027075803_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (529kB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (225kB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (529kB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (225kB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (346kB) | Request a copy
	Text RAMA_44201_08011281722061_0019077302_0027075803_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy

Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Obesitas, Naïve Bayes, Random Forest
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Mr. Abu Bakar S
Date Deposited:	23 Aug 2021 01:15
Last Modified:	23 Aug 2021 01:15
URI:	http://repository.unsri.ac.id/id/eprint/50992

Actions (login required)

View Item