Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree - Hertiana Bethaningtyas; Suwandi Suwandi; Chintia Dara Anggraini (Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 2019)
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%.