Ulcerative Colitis Classification on Endoscopy Image using Support Vector Machine with Image Extraction using Gray Level Co-Occurrence Matrix
DOI:
https://doi.org/10.26418/justin.v12i4.82903Keywords:
Endoscope, Gray Level Co-Occurrence Matrix, Image Classification, Support Vector Machine, Ulcerative ColitisAbstract
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%.
References
M. Kayal dan S. Shah, “Ulcerative Colitis: Current and Emerging Treatment Strategies,†J Clin Med, vol. 9, no. 1, hlm. 94, Des 2019, doi: 10.3390/jcm9010094.
A. Dewangga, C. Saputra, M. N. A. Sahid, dan A. P. Gani, “Ekstrak Etanolik Seledri (Apium graveolens L.) Memperbaiki Indeks Aktivitas Penyakit Kolitis Ulseratif dan Makroskopik Panjang Kolon Pada Tikus Yang di Induksi Asam Asetat,†JPSCR: Journal of Pharmaceutical Science and Clinical Research, vol. 7, no. 1, hlm. 71, Mar 2022, doi: 10.20961/jpscr.v7i1.55884.
T. Kaenkumchorn dan G. Wahbeh, “Ulcerative Colitis: Making the Diagnosis,†1 Desember 2020, W.B. Saunders. doi: 10.1016/j.gtc.2020.07.001.
A. Amatullah dan S. Miro, “Pankolitis Akibat Kolitis Ulseratif,†Health and Medical Journal, vol. 3, no. 2, hlm. 43–50, Jul 2021, doi: 10.33854/heme.v3i2.653.
L. Du dan C. Ha, “Epidemiology and Pathogenesis of Ulcerative Colitis,†Gastroenterol Clin North Am, vol. 49, no. 4, hlm. 643–654, Des 2020, doi: 10.1016/j.gtc.2020.07.005.
S. Franjić, “Briefly about Ulcerative Colitis,†J Emerg Med OA, vol. 1, no. 1, hlm. 1–5, Jul 2023, doi: 10.33140/JEMOA.
P. A. Wiratmo, Y. Hijriyati, dan E. Sumiati, “IDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN KECEMASAN PASIEN DALAM MENJALANI ENDOSKOPI,†Journals of Ners Community, vol. 13, no. 1, hlm. 7–17, Jun 2022, doi: 10.55129/jnerscommunity.v13i1.1645.
Zahrah Malidia, Yuni Susilowati, dan Siti Nurhasanah, “Pengaruh Edukasi Persiapan Endoskopi Terhadap Kepatuhan Pasien Melaksanakan Persiapan Endoskopi,†Jurnal Kesehatan, vol. 8, no. 1, hlm. 87–99, Mei 2019, doi: 10.37048/kesehatan.v8i1.155.
S. Setiyono, H. M. Suud, H. A. Faizah, dan I. S. Helwandi, “Efektivitas Pengolahan Citra dengan Metode K-Nearest Neighbor dan Gray Level Co-Occurrence Matrix untuk Monitoring Pembajakan Tanah dengan Bajak Piring,†Agroteknika, vol. 5, no. 1, hlm. 14–25, Jun 2022, doi: 10.55043/agroteknika.v5i1.125.
E. Suryati, A. Ari Aldino, N. Penulis Korespondensi, dan E. Suryati Submitted, “Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine (SVM),†Jurnal Teknologi dan Sistem Informasi, vol. 4, no. 1, hlm. 96–106, Mar 2023, doi: 10.33365/jtsi.v4i1.2445.
S. Handayani, A. Zudhi, dan R. Shofiati, “Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of Batik Bekasi Motifs,†INTELMATICS, hlm. 67–72, 2022.
S. A. Naufal, A. Adiwijaya, dan W. Astuti, “Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray,†JURIKOM (Jurnal Riset Komputer), vol. 7, no. 1, hlm. 162, Feb 2020, doi: 10.30865/jurikom.v7i1.2014.
F. Agustina dan Z. A. Ardiansyah, “Identifikasi Citra Daging Ayam Kampung dan Broiler Menggunakan Metode GLCM dan Klasifikasi-NN Image Identification of Local Chicken Meat and Broiler Chicken MeatUsing GLCM Method and K-NN Classification,†2020.
L. Luthfiana, J. C. Young, dan A. Rusli, “Implementasi Algoritma Support Vector Machine dan Chi Square untuk Analisis Sentimen User Feedback Aplikasi,†Ultimatics : Jurnal Teknik Informatika, vol. 12, no. 2, hlm. 125–126, Des 2020, doi: 10.31937/ti.v12i2.1828.
V. R. Joseph, “Optimal ratio for data splitting,†Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 15, no. 4, hlm. 531–538, Agu 2022, doi: 10.1002/sam.11583.
D. Septhya dkk., “Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru,†MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 1, hlm. 15–19, Mei 2023, doi: 10.57152/malcom.v3i1.591.
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