Institut Teknologi Nasional |
v INTISARI
Glaukoma merupakan neuropati optik kronis ditandai dengan pencekungan diskus optikus dan pengecilan lapang pandang, dapat disertai dengan peningkatan tekanan intraokuler. Pada glaukoma akan terdapat melemahnya fungsi mata dengan terjadinya cacat lapang pandang dan kerusakan anatomi berupa ekskavasi (penggaungan) serta degenerasi pupil saraf optik yang dapat berakhir dengan kebutaan.
Dalam penelitian ini penulis menerapkan Relevance Vector Machine untuk mengidentifikasi glaukoma pada retina manusia. Relevance Vector Machine adalah sebuah metode pembelajaran mesin yang diadaptasi dari Bayesian Framework. Performa sistem diukur berdasarkan nilai akurasi, presisi, recall, dan F-Measure. Eksperimen dilakukan pada dataset glaukoma yang diperoleh nilai rata-rata dari segi accuracy, precision, recall, dan f-measure berturut-turut adalah accuracy 80%, precision 86%, recall 80%, dan f-measure 79%. Nilai- nilai tersebut dipengaruhi oleh jumlah dataset dari citra training, citra validation, dan citra uji. Berdasarkan hasil tersebut, Relevance Vector Machine yang diusulkan memiliki kinerja accuracy 80%.
Kata Kunci: Indentifikasi, Glaukoma, Relevance Vector Machine, Gray-level Cooncurrence Matrix
Institut Teknologi Nasional |
vi ABSTRACT
Glaucoma is an optic neuropathy with control of optic disc curvature and visual field reduction, which may be accompanied by increased intraocular pressure. In glaucoma there will be a weakening of eye function with visual field damage and anatomical damage in the form of excavation (echoing) and degeneration of the pupil's optic nerve which can lead to blindness. In this study, the authors applied the Relevance Vector Machine to identify glaucoma in the human retina.
Relevance Vector Machine is a machine learning method adapted from the Bayesian Framework. Accurate system performance based on accuracy, precision, recall, and F-Measure values. Experiments were carried out on the glaucoma dataset which obtained the average value in terms of accuracy, precision, recall, and f-measure, respectively, an accuracy of 80%, 86% precision, 80% recall, and 79% f-measure.
These values are obtained by the number of datasets from image training, image validation, and image testing. Based on these results, the proposed Relevance Vector Machine has an accuracy performance of 80%
Keyword: Identification, Glaucoma, Relevance Vector Machine, Gray- level Cooncurrence Matrix
