Fakultas Teknologi Informasi

Optimasi decision tree menggunakan particle swarm optimization untuk identifikasi penyakit mata berdasarkan analisis tekstur

Penulis
Dosen:
  1. TONI ARIFIN
  2. ASTI HERLIANA
Tanggal Terbit
31 Januari 2020
Kategori
Jurnal Nasional Terakreditasi [SINTA 2]
Penerbit
Jurnal Teknologi dan Sistem Komputer
Kota / Negara
Ponegoro / Indonesia
Volume
8(1), 2020
Halaman
59-63
ISSN
2620-4002
E-ISSN
2338-0403
E-ISBN
https://doi.org/10.14710/jtsiskom.8.1.2020.59-63
URL
https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13454
Abstrak
The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.