Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases


  • Valencya Lestari Master of Informatics Program, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia
  • Herman Mawengkang 2Department of Mathematics, Universitas Sumatera Utara, Medan, Indonesia
  • Zakarias Situmorang Faculty of Computer Science, Universitas Katolik Santo Thomas, Medan, Sumatera Utara, Indonesia




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%.


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How to Cite

Lestari, V., Mawengkang, H. ., & Situmorang, Z. . (2023). Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 35-47.

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