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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Valencya Lestari
Master of Informatics Program, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia
Computer Science & IT

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Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases Valencya Lestari; Herman Mawengkang; Zakarias Situmorang
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11998

Abstract

Artificial neural networks are information processing systems that have certain performance characteristics in common with biological neural networks. Backpropagation is a method in artificial neural networks that uses supervised learning. Backpropagation has a weakness in reaching the convergence level. The convergence rate is the difference from the mean square error value. The mean square error is the difference between the target value and the actual value. One way to increase the convergence rate is to provide input values. in this study using the nguyen widrow backpropagation method. The network will predict Tuberculosis cases. Data sourced from the North Sumatra Provincial Health Office from 2019 to 2021. architectural testing with a learning rate ranging from -0.5 to 0.5 and momentum ranging from 0 to 1 obtained a learning rate of 0.5, the epoch process stops at the 172nd iteration with an achievement gradient of 0.0001598 and the R value for training data is 0.99841 which means it is very good because it is close to 1 with an accuracy rate of 81.82%.  
Co-Authors Herman Mawengkang Zakarias Situmorang