Abstract
The number of Chronic Kidney Disease patient increased year by year while it doesn’t following by sufficient human resources and infrastructure needs the information of Chronic Kidney Disease patient prediction. Prediction of Chronic Kidney Disease patient is necessary to be done as an anticipation for preparing the better human resources and infrastructure that will effect to patient survival rate. In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient. Artificial Neural Network has the ability in time series data prediction, such as the number of Chronic Kidney Disease year by year. But, backpropagation artificial neural network has a weakness in weight inisialization which taken unoptimally that could cause bad convergence speed. Particle swarm optimization will resolve the backpropagation artificial neural network weakness by weights optimization that will used in backpropagation artificial neural network. The Artificial Neural Network and Particle Swarm Optimization have several parameters, such as the number of hidden layer neuron, learning rate, and swarm. This research is using RSUD Banyumas Chronic Kidney Disease patient data in 2011 until 2020. Matlab R2019a used in this research as a software to predict chronic kidney disease patient data. The test results shows the prediction accuracy based on Mean Squared Error value is 0,0370 using 12-16-1 artificial neural network architecture, 0.005 learning rate, 1250 epochs and 50 swarms
Highlights
Penyakit ginjal kronis (PGK) termasuk dalam jenis penyakit tidak menular yang memiliki prevalensi yang terus meningkat dari tahun ke tahun bersamaan dengan pertumbuhan jumlah penduduk usia lanjut [1]
The number of Chronic Kidney Disease patient increased year by year while it doesn't following by sufficient human resources and infrastructure needs the information of Chronic Kidney Disease patient prediction
necessary to be done as an anticipation for preparing the better human resources and infrastructure
Summary
Peningkatan jumlah pasien penderita ginjal kronis dari tahun ke tahun, namun tidak diikuti dengan sumber daya manusia dan sarana prasarana yang memadai dalam penanganannya memerlukan informasi mengenai perkiraan jumlah pasien penyakit ginjal kronis. Prediksi terhadap jumlah pasien penderita penyakit ginjal kronis perlu dilakukan sebagai bentuk antisipasi dalam mempersiapkan sumber daya manusia dan sarana prasarana dengan lebih baik yang akan berpengaruh terhadap tingkat pertahanan hidup pasien. Penelitian ini menggunakan kombinasi algoritma jaringan saraf tiruan backpropagation dan particle swarm optimization dalam memprediksi jumlah pasien penderita penyakit ginjal kronis. Jaringan saraf tiruan memiliki kemampuan dalam melakukan prediksi terhadap data time series seperti jumlah pasien penderita ginjal kronis dari tahun ke tahun. Algoritma particle swarm optimization memiliki peran dalam mengoptimalkan nilai bobot yang akan digunakan pada algoritma jaringan saraf tiruan backpropagation. Kata Kunci: Backpropagation, Jaringan Saraf Tiruan, Particle Swarm Optimization, Penyakit Ginjal Kronis, Prediksi
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