Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree
This paper presents the study of pathological vocal cords classification using digital image processing. There are six classifications of vocal cords, including the normal vocal cords condition. Before the classification process, image vocal cords are extracted to obtain the characteristics or infor...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Universitas Gadjah Mada
2019-05-01
|
Series: | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
Subjects: | |
Online Access: | http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/506 |
id |
doaj-747fac624f2e4ab58507c6e08bbc7284 |
---|---|
record_format |
Article |
spelling |
doaj-747fac624f2e4ab58507c6e08bbc72842020-11-24T20:48:59ZengUniversitas Gadjah MadaJurnal Nasional Teknik Elektro dan Teknologi Informasi2301-41562460-57192019-05-018210.22146/jnteti.v8i2.506436Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision TreeHertiana BethaningtyasSuwandi SuwandiChintia Dara AnggrainiThis paper presents the study of pathological vocal cords classification using digital image processing. There are six classifications of vocal cords, including the normal vocal cords condition. Before the classification process, image vocal cords are extracted to obtain the characteristics or information of objects in the image. In this study, shape measurement is used to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get the binary image, this study uses a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, which is Chan-Vese algorithm. The result of this study can optimize glottis contour extraction and results of the classification training process using Decision Tree algorithm obtains an accuracy of 98.3%http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/506Pita Suara;Pengukuran Bentuk;Algoritme Chan-Vese;Algoritme Pohon Keputusan |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hertiana Bethaningtyas Suwandi Suwandi Chintia Dara Anggraini |
spellingShingle |
Hertiana Bethaningtyas Suwandi Suwandi Chintia Dara Anggraini Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree Jurnal Nasional Teknik Elektro dan Teknologi Informasi Pita Suara;Pengukuran Bentuk;Algoritme Chan-Vese;Algoritme Pohon Keputusan |
author_facet |
Hertiana Bethaningtyas Suwandi Suwandi Chintia Dara Anggraini |
author_sort |
Hertiana Bethaningtyas |
title |
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree |
title_short |
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree |
title_full |
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree |
title_fullStr |
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree |
title_full_unstemmed |
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree |
title_sort |
sistem klasifikasi kondisi pita suara dengan metode decision tree |
publisher |
Universitas Gadjah Mada |
series |
Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
issn |
2301-4156 2460-5719 |
publishDate |
2019-05-01 |
description |
This paper presents the study of pathological vocal cords classification using digital image processing. There are six classifications of vocal cords, including the normal vocal cords condition. Before the classification process, image vocal cords are extracted to obtain the characteristics or information of objects in the image. In this study, shape measurement is used to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get the binary image, this study uses a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, which is Chan-Vese algorithm. The result of this study can optimize glottis contour extraction and results of the classification training process using Decision Tree algorithm obtains an accuracy of 98.3% |
topic |
Pita Suara;Pengukuran Bentuk;Algoritme Chan-Vese;Algoritme Pohon Keputusan |
url |
http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/506 |
work_keys_str_mv |
AT hertianabethaningtyas sistemklasifikasikondisipitasuaradenganmetodedecisiontree AT suwandisuwandi sistemklasifikasikondisipitasuaradenganmetodedecisiontree AT chintiadaraanggraini sistemklasifikasikondisipitasuaradenganmetodedecisiontree |
_version_ |
1716807223151165440 |