OPTIMASI JARINGAN SARAF TIRUAN UNTUK DIAGNOSIS PENYAKIT DIABETES INDIAN PIMA
DOI:
https://doi.org/10.26594/teknologi.v6i1.560Abstract
ABSTRAK
Untuk mengetahui kondisi seseorang menderita diabetes harus dengan melakukan beberapa tes pada labolatorium, US National Institute of Diabetes telah melakukan uji untuk penyakit Diabetes sesuai dengan kriteria Organisasi Kesehatan Dunia yang dilakukan pada sejumlah perempuan yang berusia 21 tahun, dari warisan Pima India dan tinggal di dekat Phoenix, Arizona sebanyak 768 objek. Jumlah data Diabetes Indian Pima yaitu sebanyak 768 data. Untuk percobaan ini, data tersebut dibagi menjadi dua yaitu 80% sebagai data training dan 20% sebagai data testing. Dengan menggunakan jaringan saraf tiruan Backpropagation, data tersebut dikembangkan untuk diagnosa penyakit Diabetes. Hal ini diharapkan dapat digunakan untuk memprediksi potensi seseorang terserang Diabetes. Klasifikasi jaringan saraf tiruan Backpropagation ini dioptimasi menggunakan metode Nguyen Widrow agar rule yang dihasilkan lebih signifikan atau rule yang dihasilkan dapat meningkatkan akurasi. Pengujian menggunakan data testing Diabetes dan inisialisasi Nguyen Widrow, maka dihasilkan tingkat akurasi sebesar 100%. Sedangkan jika menggunakan inisialisasi bobot random, maka dihasilkan tingkat akurasi sebesar 50%.
Kata Kunci: Backpropagation ,Diabetes, Jaringan Saraf Tiruan, Nguyen Widrow.
ABSTRACT
To determine the condition of a person suffering from diabetes need to do some tests in laboratories, the US National Institute of Diabetes has been test for Diabetes in accordance with the criteria of the World Health Organization conducted a number of women aged 21 years, from the legacy of Pima Indians and stay near Phoenix , Arizona as many as 768 objects. The amount of data Pima Indian Diabetes as many as 768 data. For this experiment, the data is divided into two: 80% as training data and 20% as a data testing. By using a neural network Backpropagation, the data developed for the diagnosis of Diabetes. It is expected-kan can be used to predict the potential of a person develops diabetes. Classification neural network Backpropagation is optimized using methods Nguyen Widrow that produced more significant rule or rule produced can improve accuracy. Diabetes testing using testing and initialization of data Nguyen Widrow, then the resulting accuracy rate of 100%. Whereas if you use random weight initialization, then produced a 50% accuracy rate.
Keywords: Backpropagation ,Diabetes, Neural Network, Nguyen Widrow
References
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