Perbandingan Tingkat Akurasi Algoritma Restricted Boltzmann Machine Dan Backpropagation Dalam Mendeteksi Penyakit Diabetes Melitus
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Institusion
Universitas Muhammadiyah Surakarta
Author
Hajiati, Sri
, Dimas Aryo Anggoro, S.Kom., M.Sc
, Dimas Aryo Anggoro, S.Kom., M.Sc
Subject
H Social Sciences (General)
Datestamp
2022-01-05 06:36:16
Abstract :
Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of diabetics worldwide. The increase in the number of diabetes is caused by the delay in establishing the diagnosis of the disease and symptoms that are not realized by the public. Therefore, initial action is needed as a solution that requires the most appropriate and accurate data mining in the management of diabetes mellitus. The algorithm used is an artificial neural network algorithm, namely Restricted Boltzmann Machine and Backpropagation. The purpose of this study is to compare the two yahoo and see which yahoo can produce high accuracy and determine which yahoo is more accurate than the two yahoo in detecting diabetes mellitus. This research uses data collection methods, data preprocessing, data processing and evaluation models. The results obtained from this study on the Restricted Boltzman Machine algorithm of 82.02% and the Backpropagation algorithm of 87.01% using the normalization method. It can be concluded that the Backpropagation algorithm produces a more accurate and accurate value in the control of diabetes mellitus. The Backpropagation algorithm has a sensitivity of 85.71% and a specificity of 87.66% in detecting diabetes mellitus. From the results obtained, it is hoped that it can help the community in diabetes mellitus by providing the right and accurate algorithm.
Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of diabetics worldwide. The increase in the number of diabetes is caused by the delay in establishing the diagnosis of the disease and symptoms that are not realized by the public. Therefore, initial action is needed as a solution that requires the most appropriate and accurate data mining in the management of diabetes mellitus. The algorithm used is an artificial neural network algorithm, namely Restricted Boltzmann Machine and Backpropagation. The purpose of this study is to compare the two yahoo and see which yahoo can produce high accuracy and determine which yahoo is more accurate than the two yahoo in detecting diabetes mellitus. This research uses data collection methods, data preprocessing, data processing and evaluation models. The results obtained from this study on the Restricted Boltzman Machine algorithm of 82.02% and the Backpropagation algorithm of 87.01% using the normalization method. It can be concluded that the Backpropagation algorithm produces a more accurate and accurate value in the control of diabetes mellitus. The Backpropagation algorithm has a sensitivity of 85.71% and a specificity of 87.66% in detecting diabetes mellitus. From the results obtained, it is hoped that it can help the community in diabetes mellitus by providing the right and accurate algorithm.
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