Perbandingan Metode Pembelajaran Mesin Berbasis Parametrik dan Non-Parametrik Untuk Klasifikasi Diabetic Retinopathy Imagery
DOI:
https://doi.org/10.36085/jsai.v4i2.1668Abstract
Untuk mendeteksi kerusakan retina dapat dilakukan bantuan algoritmapembelajaran mesin. Klasifikasi citra dengan menggunakan machine learning
techniques (MLTs) dapat membantu proses penentuan pasien penderita
diabetic retinopathy (DR). Teknik machine learning yang digunakan dapat
dikelompokkan menjadi nonparametric (support vector machine) dan
parametric (logistic regression). Tahap penelitian termasuk persiapan,
ekstraksi fitur, normalisasi, klasifikasi, evaluasi dilakukan terhadap dataset
gambar digital fundus yang disediakan oleh EyePACS. Model klasifikasi
menggunakan model nonparametric (support vector machine) dan parametric
(logistic regression). Sebagai hasil, metode logistic regression mendapatkan
hasil akurasi (accuracy) sebesar 74%, recall sebesar 74%, presisi (precision)
sebesar 60% dan F1-score sebesar 63%. Selain itu, metode support vector
machine mendapatkan hasil akurasi (accuracy) sebesar 74%, recall sebesar
74%, presisi (precision) sebesar 55% dan F1-score sebesar 63%
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