KLASIFIKASI TINGKAT KERAPATAN VEGETASI DENGAN PARAMETER NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN)

LESTARI, NOVIA TRI and Sukemi, Sukemi (2019) KLASIFIKASI TINGKAT KERAPATAN VEGETASI DENGAN PARAMETER NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN). Undergraduate thesis, sriwijaya university.

	Text RAMA_56201_09011281419047.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) | Request a copy
	Text RAMA_56201_09011281419047_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
Preview	Text RAMA_56201_09011281419047_0003126604_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (10MB) | Preview
	Text RAMA_56201_09011281419047_0003126604_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (477kB) | Request a copy
	Text RAMA_56201_09011281419047_0003126604_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (536kB) | Request a copy
	Text RAMA_56201_09011281419047_0003126604_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011281419047_0003126604_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (180kB) | Request a copy
	Text RAMA_56201_09011281419047_0003126604_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (301kB) | Request a copy
	Text RAMA_56201_09011281419047_0003126604_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (574kB) | Request a copy

Abstract

Vegetation is a collection of plants that occupy an ecosystem that serves as the vegetation cover of the earth's surface. This study was made to test the system in the classification of land vegetation density levels in an area using remote sensing technology by utilizing the image data landsat8. Image quality improvement by performing geometric correction, radiometric correction, contrast stretching and smoothing filter mean. NDVI is used as a parameter in determining the density of vegetation. Results NDVI values ​​were grouped into five clusters using k-means. Of clusters - clusters produced 5 centroid value referenced in the classification of vegetation density level of the region. Tested region is a region of the island of Java, Indonesia. The results of the classification of the area with Path / Row 119/065 has a percentage of the value of vegetation by 35% with the density of the vegetation is very low. Areas with Path / Row 120 has a percentage of 19% vegetation density is very low density level, and 3% lower vegetation density levels, and regions with Path / Row 121/065 has a percentage of 34% is very low density level, 26% of the level of low density and 4 percent of the rod vegetation density was.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	K-NN, K-Means, NDVI, Perbaikan Kualitas Citra, Vegetasi.
Subjects:	Q Science > Q Science (General) > Q350-390 Information theory
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 3322 not found.
Date Deposited:	25 Nov 2019 05:56
Last Modified:	25 Nov 2019 05:56
URI:	http://repository.unsri.ac.id/id/eprint/18170

Actions (login required)

View Item