DETEKSI KADAR KOLESTEROL MELALUI IRIS MATA MENGGUNAKAN IMAGE PROCESSING DENGAN METODE JARINGAN SYARAF TIRUAN DAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM)

  • Agung Saputra Prodi Elektro Fakultas Teknik Universitas Pancasila, Srengseng Sawah, Jagakarsa, Jak-Sel 12640
  • Wisnu Broto Prodi Elektro Fakultas Teknik Universitas Pancasila, Srengseng Sawah, Jagakarsa, Jak-Sel 12640
  • Liani Budi R. 3Prodi Elektro Fakultas Teknik Universitas Pancasila, Srengseng Sawah, Jagakarsa, Jak-Sel 12640

Abstract

Abstrak

Iridologi merupakan suatu teknik analisis penyakit dan kelemahan tubuh berdasarkan bentuk dan struktur di dalam iris mata (berada di sekeliling pupil). Analisa iridologi biasanya dilakukan secara manual oleh praktisi iridologi atau dengan orang yang berpengalaman karena iridologi bisa dipelajari. Tujuan dari penelitian ini adalah untuk mendeteksi kolesterol seseorang tinggi atau normal menggunakan pelatihan jaringan syaraf tiruan dan data inputnya menggunakan metode perbandingan tekstur Gray Level Co-Occurrence Matrix (GLCM). Image input dengan ukuran 300x300 pixel dimasukkan ke program untuk dilakukan tahap preprocessing berupa grayscale, noise remover, kontras image, pemaparan image polar, dan cropping image. Dari hasil preprocessing, dihitung nilai rata-rata data statistik menggunakan metode GLCM dengan jarak 2 pixel. Berdasarkan hasil pengujian data training, persentase keakuratan program sebesar 97,5%. Untuk pengujian image uji selain image latih, persentase keakuratan sebesar 95%. Keakuratan image uji berdasarkan pemeriksaan medis sebesar 81,81%.

Kata-kata kunci: Iridologi, Jaringan Syaraf Tiruan, GLCM.

Abstract

Iridology is an analytical technique based on the body's disease and weakness in the shape and structure of the iris of the eye (located around the pupil). Iridology analysis is usually done manually by iridology practitioner or by someone who is experienced in iridology because iridology can be learned. The purpose of this study is to detect high cholesterol or normal cholesterol using artificial neural network training and data input using Gray Level Co-Occurrence Matrix (GLCM) texture comparison method. Image input with 300x300 pixel incorporated into the program to do preprocessing stages such as grayscale, noise remover, image contrast, image exposure polar, and cropping the image. From the results of preprocessing, the average value of statistical data is calculated using GLCM methods with the distance is two pixels. Based on the results of testing the training data, the percentage of program accuracy is 97.5%. Based on the results of testing other image training, the accuracy percentage is 95%. The accuracy of testing image based on a medical examination is 81.81%.

Keywords: Iridologi, Artificial Neural Network, GLCM.

 

Published
2017-10-30
Section
Computation and Instrumentation Physics