Aplikasi zoom cloud meetings yang mulai booming digunakan sekarang ini karena adanya pandemi virus corona, sehingga membuat semua kegiatan dilakukan secara virtual. Zoom cloud meetings merupakan aplikasi yang memiliki berbagai fitur termasuk video & audio conference. Pada penelitian ini penulis menggunakan metode Naïve Bayes dan Support Vector Machine dalam menganalisa label sentimen positif atau negatif pada ulasan para pengguna aplikasi zoom di Google Play Store. Jumlah dataset setelah prepocessing menjadi 1.007 record. Data hampir seimbang dengan label positif sebanyak 546 dan label negatif 461 ulasan. Evaluasi model menggunakan 10 fold cross validation diperoleh nilai akurasi dan nilai AUC dari masing-masing algoritma yaitu untuk NB nilai akurasi = 74,37% dan nilai AUC = 0,659. Sedangkan untuk algoritma SVM nilai akurasi = 81,22% dan nilai AUC = 0,886. Dalam penelitian ini dapat diketahui bahwa tingkat akurasi yang didapatkan algoritma Support Vector Machine (SVM) lebih unggul 6,85% dibandingkan algoritma Naïve Bayes (NB). Kata Kunci— Zoom Cloud Meetings, Google Play Store, Virus Corona, Naïve Bayes, Support Vector Machine. Abstract— Zoom cloud meetings application that began to boom is used today because of the corona virus pandemic, so that all activities are carried out virtually. Zoom cloud meetings is an application that has various features including video & audio conferencing. In this study the authors used the Naïve Bayes method and Support Vector Machine in analyzing positive or negative sentiment labels on the zoom users' reviews on the Google Play Store. The number of datasets after prepocessing is 1,007 records. The data is almost balanced with 546 positive labels and 461 negative labels. Evaluation of the model using 10 fold cross validation obtained accuracy values and AUC values from each algorithm, namely for NB, the accuracy value = 74.37% and the AUC value = 0.659. As for the SVM algorithm the accuracy value = 81.22% and the AUC value = 0.886. In this study it can be seen that the accuracy obtained by the Support Vector Machine (SVM) algorithm is 6.85% superior to the Naïve Bayes (NB) algorithm.

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.

This study aims to improve the performance of teachers who are considered still lacking. For that done aninnovation by applying academic supervision. This research is a school action research, which carried out twocycles with four stages: planning, implementation, observation, and reflection. The results of this study indicatethat the application of academic supervision can improve the performance of elementary school teachers. This isevidenced by the results of research: In the planning indicators in the first cycle to get a score of 60.00 increasedin cycle II with a score of 75.00%. In the implementation of learning indicators obtained a score of 55.00%increased in cycle II with a score of 70.00%. And on learning scoring indicator get score 55,00% increase withscore 70,00%. In addition, the average teacher performance also increased in the first cycle of teacherperformance average is 56.66 with enough category, in the second cycle increased to 71.67 with Good category.

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.