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All Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Nurrohman, Agni
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Computer Science & IT

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Ulcerative Colitis Classification on Endoscopy Image using Support Vector Machine with Image Extraction using Gray Level Co-Occurrence Matrix Nurrohman, Agni; Hoeronis, Irani; Lukmana, Hen Hen
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.82903

Abstract

Ulcerative colitis or inflammation of the colon is a chronic inflammatory disorder characterized by mucosal inflammation involving the large intestine (colon) and leading to the anus (rectum). The number of cases of ulcerative colitis ranges from 90-505 people out of 100,000 people in Northern Europe and North America, less common in Western and Southern European regions as well as at least 10 times less in Asia, Africa and Oriental populations. This study aims to classify endoscopic images with the Support Vector Machine method with the results of feature extraction using Gray Level Co-Occurrence Matrix. The dataset used is the kvasir dataset with the number of datasets used in this study totaling 1990 with each class, namely the healthy class and the ulcerative colitis class, having 995 images. Endoscopy results in the form of digital images captured using a small camera inserted into the patient's gastrointestinal tract. In this study, the accuracy model of Ulcerative Colitis classification was calculated using the results of endoscopy image feature extraction with GLCM feature extraction using SVM classification with RBF kernel. The search for hyperparameter values is carried out to find the best C and gamma values so that this study has model accuracy results which previously had an accuracy of 86.45% to 90.85%, a precision value of 91.58%, a recall value of 90.68% and an f1-score value of 91.12%.
Co-Authors Irani Hoeronis Lukmana, Hen Hen