Implementasi Feed Forward Neural Network (FFNN) dalam Memprediksi Penyakit Diabetes


DOI: https://doi.org/10.30998/.v3i1.3048

Septian Wulandari(1*), Dian Novita(2),

(1) Universitas Indraprasta PGRI
(2) Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract


Diabetes is a non-communicable disease that has the seventh highest mortality rate in the world. Diabetes causes a high frequency of thirst, frequent urination, decreased body weight and endurance, resulting in the body becoming easily weak and sick. Not infrequently, patients suffering from diabetes die at a relatively young age. This research aims to identify or predict diabetes using the Artificial Neural Network Training (ANN) method, namely Feed Forward Neural Network (FFNN). The variables used in the input values are personal data and medical records, totaling 8 independent variables and 1 dependent variable which is used as training and testing data. The data was processed using R software and the resulting error value was 0.002857, meaning less than 1%, proving that the resulting model was accurate enough as shown by the proximity of the target to the output results and the steps or number of iterations required by the system during the calculation process and 265 iterations were obtained. The Cohen's Kappa value or Kappa value in this study is 1 or 100%, meaning that there is perfect agreement between the prediction model and its accuracy value.

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DOI: https://doi.org/10.30998/.v3i1.3048

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