EFISIENSI PSO DALAM PENGOPTIMALAN NEURAL NETWORK UNTUK CKD

Titin Prihatin, Sartini Sartini, Witriana Endah Pangesti, Yudhistira Yudhistira, Rachmat Suryadithia

Abstract


Chronic Kidney Disease (CKD) is often detected at an advanced stage, when treatment becomes more difficult. The challenge in early detection of CKD lies in the insignificance of the symptoms in the early stages. Various methods and algorithms have been developed to improve the accuracy of CKD predictions, but the Neural Network (NN) method has not been optimized effectively with algorithm optimization. This study aims to examine the use of Particle Swarm Optimization (PSO) in optimizing NN models for CKD classification and diagnosis. Model evaluation is carried out by comparing performance results before and after optimization using accuracy, precision, recall and AUC metrics. The results of optimization using PSO succeeded in increasing model accuracy from 98.00% to 99.00%, as well as increasing precision from 95.15% to 97.50%. The NN model has excellent capabilities in detecting CKD both before and after optimization. Although the performance improvement is not very significant, PSO is proven to be effective in reducing false positive errors. This study concludes that the combination of NN and PSO can improve the accuracy of CKD predictions and provide important insights into the most influential predictor variables. It is hoped that further research will be able to test the model on larger and more diverse datasets, as well as explore other optimization methods to improve prediction results.

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References


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DOI: http://dx.doi.org/10.36723/juri.v16i2.707

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