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Ika Candradewi
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Electrical & Electronics Engineering

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Sistem Klasifikasi Tingkat Keparahan Retinopati Diabetik Menggunakan Support Vector Machine Taufiq Galang Adi Putranto; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 1 (2018): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (844.602 KB) | DOI: 10.22146/ijeis.31206

Abstract

Diabetic retinopathy is a vision disorder disease that can cause damage to the retina of the eye that will have a direct impact on the disruption of vision of the patient. The diabetic retinopathy phase is classified into four types (normal, mild NPDR, moderate NPDR (Non-Proliferative Diabetic Retinopathy), and severe NPDR). Retinal of eye data of diabetic retinopathy patients treated from the MESSIDOR database. By applying image processing, the retinal image of the eye in extraction using the area features extraction from the detection of exudate, blood vessels, microaneurysms, and texture feature extraction Gray Level Co-occurrence Matrix. The extracted results classified using the Support Vector Machine method with the Radial Basis Function (RBF) kernel. Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.The results of classification show the best value using 6 statistical features ie, contrast, homogeneity, correlation, energy, entropy and inverse difference moment in the direction of 45 degrees with the RBF kernel. The result of classification research system on 240 data training and 60 data testing yields an average accuracy is 95.93%, the value of specificity is 97.29%, and a sensitivity rating is  91.07%. From the research result, using RBF kernel get the best accuracy result than using kernel polynomial or kernel linear.
Co-Authors Taufiq Galang Adi Putranto