@article{Agustyaningrum_Dahlia_Pahlevi_2023, title={Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox}, volume={5}, url={http://ejournal.kresnamediapublisher.com/index.php/jri/article/view/217}, DOI={10.34288/jri.v5i3.217}, abstractNote={<p>Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. &nbsp;Injury-related mortality in primates ranges from 1 to 10%. &nbsp;Data mining is a method for analyzing data. &nbsp;Deep neural networks and traditional machine learning methods are used in data analysis. &nbsp;The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. &nbsp;The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm’s accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. &nbsp;Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. &nbsp;The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. &nbsp;According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.</p>}, number={3}, journal={Jurnal Riset Informatika}, author={Agustyaningrum, Cucu Ika and Dahlia, Rizka and Pahlevi, Omar}, year={2023}, month={Jun.}, pages={253–262} }