KLASIFIKASI JENIS PASIR MATERIAL BANGUNAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) BERDASARKAN EKSTRAKSI CIRI TEKSTUR DAN WARNA

type of sand has a great influence on the results of construction so that we must be able to choose the type of sand that suits the needs of the building. In this study, sand type image classification will be classified using Support Vector Machine (SVM) classification method combined with Gray Leve...

Full description

Saved in:
Bibliographic Details
Main Author: Yulia Astutik, (Author)
Format: Book
Published: 2022-07-07.
Subjects:
Online Access:Link Metadata
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 repoupnvj_19748
042 |a dc 
100 1 0 |a Yulia Astutik, .  |e author 
245 0 0 |a KLASIFIKASI JENIS PASIR MATERIAL BANGUNAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) BERDASARKAN EKSTRAKSI CIRI TEKSTUR DAN WARNA 
260 |c 2022-07-07. 
500 |a http://repository.upnvj.ac.id/19748/1/ABSTRAK.pdf 
500 |a http://repository.upnvj.ac.id/19748/2/AWAL.pdf 
500 |a http://repository.upnvj.ac.id/19748/3/BAB%201.pdf 
500 |a http://repository.upnvj.ac.id/19748/4/BAB%202.pdf 
500 |a http://repository.upnvj.ac.id/19748/5/BAB%203.pdf 
500 |a http://repository.upnvj.ac.id/19748/6/BAB%204.pdf 
500 |a http://repository.upnvj.ac.id/19748/9/BAB%205.pdf 
500 |a http://repository.upnvj.ac.id/19748/10/Daftar%20Pustaka.pdf 
500 |a http://repository.upnvj.ac.id/19748/8/RIWAYAT%20HIDUP.pdf 
500 |a http://repository.upnvj.ac.id/19748/11/lampiran.pdf 
500 |a http://repository.upnvj.ac.id/19748/14/HASIL%20PLAGIARISME.pdf 
500 |a http://repository.upnvj.ac.id/19748/13/ARTIKEL%20KI.pdf 
520 |a type of sand has a great influence on the results of construction so that we must be able to choose the type of sand that suits the needs of the building. In this study, sand type image classification will be classified using Support Vector Machine (SVM) classification method combined with Gray Level Co-Occurrence Matrix method to extract texture features and Color Moment RGB for color feature extraction. The dataset used in this research is 500 images consisting of 5 classes with the amount of data for each class is 100 image data. In the classification process, the image dataset will be divided into 80% training data and 20% testing data and then create one-vs-rest multi-class SVM classification model based on GLCM texture characteristics and Color Moment RGB colors. After the classification process is carried out, the accuracy value is 94% with an angle of 135 degrees and an image size of 250 x 250 pixels. 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
546 |a id 
690 |a QA75 Electronic computers. Computer science 
655 7 |a Thesis  |2 local 
655 7 |a NonPeerReviewed  |2 local 
787 0 |n http://repository.upnvj.ac.id/19748/ 
787 0 |n https://repository.upnvj.ac.id 
856 4 1 |u http://repository.upnvj.ac.id/19748/  |z Link Metadata