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...

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Main Authors: Hertiana Bethaningtyas, Suwandi Suwandi, Chintia Dara Anggraini
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
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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
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