This study was conducted with the aim to the clustering of provinces in Indonesia of the risk of the COVID-19 pandemic based on coronavirus disease 2019 (COVID-19) data. This clustering was based on the data obtained from the Indonesian COVID-19 Task Force (SATGAS COVID-19) on 19 April 2020. Provinces in Indonesia were grouped based on the data of confirmed, death, and recovered cases of COVID-19. This was performed using the K-Means Clustering method. Clustering generated 3 provincial groups. The results of the provincial clustering are expected to provide input to the government in making policies related to restrictions on community activities or other policies in overcoming the spread of COVID-19. Provincial Clustering based on the COVID-19 cases in Indonesia is an attempt to determine the closeness or similarity of a province based on confirmed, recovered, and death cases. Based on the results of this study, there are 3 clusters of provinces.

Guru diharapkan dapat meningkatkan kompetensi salah satunya dalam penguasaan Teknologi Informasi dan Komunikasi (TIK). Hal ini diperlukan untuk mendukung kegiatan pembelajaran yang menarik, efektif dan efisien. Oleh karenanya, perlu diadakan suatu kegiatan atau pelatihan untuk menambah pengetahuan/wawasan dan keterampilan guru tentang TIK. Pelatihan yang diberikan diharapkan dapat diaplikasikan guru dalam penggunaan media teknologi informasi untuk meningkatkan efektifitas dan efisiensi dalam kegiatan belajar mengajar. Berdasarkan kondisi yang terjadi sekarang terdapat beberapa masalah yang akan diatasi pada kegiatan ini, antara lain pemanfaatan penggunaan smartphone oleh guru dan peserta didik masih sebatas pada alat komunikasi dan hiburan, padahal dapat dioptimalkan penggunaannya sebagai media pembelajaran berbasis android. Tujuan dari kegiatan pengabdian masyarakat ini adalah untuk meningkatkan kompetensi pedagodik guru dan kemampuan guru dalam membuat media pembelajaran yang interaktif. Metode yang digunakan dalam pelatihan ini adalah ceramah, demonstrasi dan praktik. Teknik pengumpulan data menggunakan angket mengenai persepsi peserta terhadap pelaksanaan pelatihan. Indikator keberhasilan pelatihan ini meliputi tingkat pembelajaran, tingkat perilaku, tingkat reaksi dan tingkat hasil. Hasil yang diperoleh dari pengolahan data keseluruhan indikator adalah peserta setuju bahwa pelaksanaan pelatihan  telah berjalan dengan baik, dimana sebagian besar peserta berpandangan bahwa pelaksanaan pelatihan telah memberikan manfaat yang mendalam dan sangat membantu dalam peningkatan kualitas diri peserta.

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In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold crossvalidation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.

Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.

Cerebral infarction is one of the causes of ischemic stroke in the brain, and machine learning can be used in the detection of cerebral infarction in the brain. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. Support vector machine (SVM) is a machine learning method that is known for its high accuracy value. However, SVM can produce less optimal results if the data used is imbalanced. If imbalanced data is used, the resulting model will be biased. Therefore, this study uses a hybrid preprocessing method for SVM on the classification of an imbalanced cerebral infarction dataset obtained from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital. This method is a combination of several sampling methods that deal with the problem of imbalanced data and utilizes undersampling and oversampling techniques in combination with SVM. Oversampling modifying the infarction dataset through the duplication of data with a small number of classes to be balanced with a large number of data classes. While undersampling reducing data with a large number of classes to be balanced with a smaller number of data classes. Undersampling and Oversampling are combined into a hybrid method. This method is a hybrid method of the undersampling and oversampling that can be used in SVM. The results of hybrid method using SVM will be compared with the undersampling and oversampling using SVM, individually. And SVM method without preprocessing the imbalanced dataset. The accuracy of the proposed method reached 94% in our evaluations for SVM using a hybrid preprocessing method.