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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) POLYGLOT JURNAL MEDIA INFORMATIKA BUDIDARMA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Health and Nutrition Research eProceedings of Engineering
Wikky Fawwaz Al Maki
Universitas Telkom
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health

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Journal : Building of Informatics, Technology and Science

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Classification of Glaucoma Using Invariant Moment Methods on K-Nearest Neighbor and Random Forest Models Arsyan, Athalla Rizky; Al Maki, Wikky Fawwaz
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.941 KB) | DOI: 10.47065/bits.v3i4.1244

Abstract

One of the cardiovascular diseases that can interfere with eye vision is glaucoma. This disease is caused by high pressure on the inside of the eyeball to cause blindness slowly. In general, screening or early diagnosis can help prevent glaucoma, specifically by analyzing several eye components affected by pressure, including the optical disc, optical cup, and blood vessels. Thus, by blending machine learning algorithms and computer vision technology, glaucoma classification and identification can be accelerated and improved. This study applied the Invariant Moment method to extract the optical cup and blood vessel segmentation's shape, scale, and rotation features. To obtain segmentation results for these two objects, we threshold two image datasets, DrishtiGS-1 and REFUGE, and implemented the approach described in this study to analyze system performance on these datasets. For the classification method used in this study, we proposed KNN and RF models to evaluate the suitability of the methods we used on the REFUGE and DrishtiGS-1 datasets and demonstrated that both models could be used to identify glaucoma through the use of fundus images. When the datasets were merged, we obtained 81.86% and 75.86% of accuracy when using blood vessel and optical cup segmentation results, respectively
Co-Authors Amien Jafar Makrufi Arkananta, Muhammad Hafizh Arsyan, Athalla Rizky Artika, Artika Cahya Kemila, Azizah Crisannaufal, Kemal Dicky Hidayat Dodi Dodi Dzaky, Athallah Tsany Rakha Elvira Wardah Febryanti Sthevanie Fernanda Januar Pratama Galuh Ramaditya Ihsanti, Soraya Indra Bayu Muktyas Irsyad Rafi Diesta Khaidir Mauladan Nurdenara, Ciara Putri, Kania Ardhani Saputra, Muhammad Alfhi Savira Anggita Sabrina Suazini, Esa Risi Widya Hary Cahyati Yulio Ferdinand