A disease that is currently widespread today is caused by the spread of the coronavirus disease or what is commonly called COVID 19. This virus is very dangerous to health because it attacks organs in the human body from various sources, either from the air or direct touch. With the existence of COVID 19, it has an impact on all countries, especially the State of Indonesia, which consists of various islands, which are also affected by the COVID 19 virus. So that the central government takes a policy to carry out social distancing to every one to break the chain of spreading this virus, with this social distancing it has an impact on all activities that occur every day. As an impact on the learning process that usually takes place in class, it turns into online learning that uses several supporting applications in the learning process during the COVID 19 pandemic. With online learning from various applications, it attracts researchers to research with the K-Medoid Clustering Algorithm in using applications during the pandemic COVID 19.
Abstrak-Pengobatan penyakit kutil menggunakanCryotheraphy merupakan salah satu jenis pegobatan penyakit kutil yang direkomendasikan oleh beberapa pakar kesehatan. Metode yang digunakan dengan menggunakan nitrogen cair untuk pembekuan pada penyakit kutil. Dalam penelitian ini dilakukan komparasi pengujian model dengan menggunakan K-Nearest Neighbor dan Naiive Bayes untuk prediksi pengobatan penyakit kutil. Dalam proses pengujiannya, peneliti menggunakan aplikasi rapidminer untuk mengolah data dan melakukan pengujian. Hasil pengujian yang telah dilakukan menunjukkan pengujian menggunakan model K-Nearest Neighbor (K-NN) didapat nilai akurasi terbaik adalah 90,00% dengan nilai AUC sebesar 0,500 sedangkan hasil pengujian menggunakan model Naiive Bayes didapat nilai akurasi lebih kecil dibandingkan dengan model K-NN yaitu 86,67% dengan nilai AUC sebesar 0,932. Berdasarkan pengujian yang sudah dilakukan dapat disimpulkan bahwa model K-Nearest Neigbor memiliki tingkat akurasi lebih baik dibandingkan dengan model Naiive Bayes dalam prediksi pengobatan penyakit kutil menggunakan Cryotheraphy.
Abstract-Treatment of warts using Cryotheraphy is a type of wart disease treatment recommended by several health experts. The method used is using liquid nitrogen for freezing in wart diseases. In this study a model test was performed using K-Nearest Neighbor and Naiive Bayes for research on the treatment of warts. In the testing process, researchers use the rapidminer application to process data and conduct testing. The results of tests that have been carried out on testing using the K-Nearest Neighbor (K-NN) model get the best test value that is 90.00% with an AUC value of 0.500 while the test results using the Naiive Bayes model get higher values with the K-NN model that is 86 , 67% with an AUC value of 0.932. Based on testing that has been done, it can be concluded that the K-Nearest Neigbor model has a better rating compared to the Naif Bayes model in predicting treatment of warts using Cryotheraphy.
Cervical is the second most common malignant tumor in women, with 341,000 deaths worldwide in 2020, almost 80% of which occur in developing countries. One of the causes is infection with Human papillomavirus (HPV) types 16 and 18. The increasing incidence of cervical cancer in Indonesia makes this disease must be treated seriously because it is one of the main causes of death. In addition to the virus, external factors can be one of the causes. The high mortality rate in patients is caused by the patient's awareness of the emergence of cervical cancer which is only seen when it enters the final stage. One of the efforts to reduce the number of sufferers is to implement cervical cancer detection. Early detection of cervical cancer can also be identified by looking at external factors, such as behavioral factors, intentions, attitudes, norms, perceptions, motivations, social support, and empowerment. However, the data used has an imbalance in the distribution of the target class, namely more negative samples than positive ones. To overcome this, a technique called Stratified K-Fold Cross-Validation (SKCV) is used. Evaluation of the accuracy value using the Confusion matrix to determine the performance of each model. The best performance of the five classification algorithms used is 96 percent (RF), 94 percent (LR), 92 percent (XGBoost), 90 percent (KNN), and 88 percent (NB). The results show that the model formed by RF-based SKCV has the highest accuracy of other models.
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