KLASIFIKASI PENDERITA MONKEYPOX MENGGUNAKAN KNN, NAIVE BAYES, DAN RANDOM FOREST
Article Sidebar
Read Counter : 1
Download : 1
Main Article Content
Abstract
Monkey pox is a zoonotic viral infection caused by the monkey pox virus that causes a rash similar to smallpox. However, the person-to-person spread is beyond direct close contact and the mortality rate is much lower in monkey pox compared to smallpox infection. This case uses three classification methods, namely K-Nearest Neighbors, Logistic Regression, and Random Forest, to predict Monkeypox cases. The dataset was collected from a dataset on Kaggle consisting of 25,000 data, with 11 attributes and two classes: negative and positive, respectively. In this context, the classification method is used to find out how the method can predict with good accuracy based on the dataset used for training and testing divided into 60% training data and 40% test data by using test size = 0.4 as a parameter when dividing the data. The results of this study show that the Random Forest classification method produces the highest accuracy value using the accuracy, precision, recall, and f1-score parameters. The accuracy value obtained is 67%.
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Menawarkan akses terbukaReferences
[1] Curran, K. G., Eberly, K., Russell, O. O., Snyder, R. E., Phillips, E. K., Tang, E. C., ... & STI Team. (2022). HIV and sexually transmitted infections among persons with monkeypox— eight US jurisdictions, May 17–July 22, 2022. Morbidity and Mortality Weekly Report, 71(36), 1141.
[2] Gessain, A., Nakoune, E., & Yazdanpanah, Y. (2022). Monkeypox. New England Journal of Medicine, 387(19), 1783-1793.
[3] Rizk, J. G., Lippi, G., Henry, B. M., Forthal, D. N., & Rizk, Y. (2022). Prevention and treatment of monkeypox. Drugs, 82(9), 957-963.
[4] Sherwat, A., Brooks, J. T., Birnkrant, D., & Kim, P. (2022). Tecovirimat and the treatment of monkeypox—past, present, and future considerations. New England Journal of Medicine, 387(7), 579-581.
[5] Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: a review. Journal of Data Analysis and Information Processing, 8(4), 341-357.
[6] Zou, X., Hu, Y., Tian, Z., & Shen, K. (2019, October). Logistic regression model optimization and case analysis. In 2019 IEEE 7th international conference on computer science and network technology (ICCSNT) (pp. 135-139). IEEE.
[7] Shah, K., Patel, H., Sanghvi, D., & Shah, M. (2020). A comparative analysis of logistic regression, random forest and KNN models for the text classification. Augmented Human Research, 5, 1-16.
[8] Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert systems with applications, 134, 93-101.