Due to the COVID-19 pandemic, most educational institutions across the world have shifted their teaching and learning processes and put efforts into preparing online distance education to ensure education continues uninterrupted. Some did not face difficult tasks or challenges during this process because they were already implementing online or blended learning before the pandemic. However, some institutions, lecturers and students were not ready to adapt to the conditions, and it is therefore important to examine to what extent lecturers are ready to teach online. This research aims to evaluate the readiness of lecturers during a pandemic that arises unexpectedly. It also aims to investigate the weaknesses and obstacles that lecturers must overcome in order to teach an online class. This research applies a mixed-method approach. Lecturers were surveyed through online preparedness questionnaires, and several themes were constructed from the gathered qualitative data. The results show that lecturers have strong baseline technical skills to use e-learning platforms for online courses; they have quickly adapted to using a Learning Management System (LMS), and most have a tactical solution for most online classes with insufficient feasibility, but they do not have a strategic solution. Their sufficiency for teaching online courses was not optimised since they did not fully believe the learning goals could be achieved. This paper elaborates on the theoretical and practical implications.

Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean imputation and Fuzzy C-Means (FCM). Fuzzy C-Means is a clustering method that allows one part of the data to belong to two or more groups based on their membership function. The complete dataset was trained with Decision Tree classifier to observe the performance in terms of accuracy for mean and FCM method. The analysis of proposed imputation on classification shows that FCM slightly accurate compare to mean imputation method.

Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane. In the field of molecular biology, the development of microarray technology is used in data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, that’s clustering method not only the objects to be clustered, but also the properties or condition of the object. This research proposed a two-phase method for finding a bicluster. In the first phase, a parallel k-means algorithm is applied to the gene expression data. Then, in the second phase, Cheng and Church biclustering algorithm as one of biclustering method is performed to find biclusters. In this study, we discuss the implementation of two-phase method using biclustering of Cheng and Church and parallel k-means algorithm in Carcinoma tumor gene expression data. From the experimental results, we found five biclusters are formed by Carcinoma gene expression data.

Clustering is a mathematical approach that allows one to find a group of data with similar attributes. This approach is also often used in the field of computer science to group a large amounts of data. Triclustering analysis is an analysis technique on 3D data (observation—attribute—context). Triclustering analysis can group observations on several attributes and contexts simultaneously. Triclustering analysis has been frequently applied to analyze microarray gene expression data. We proposed the $\delta$-Trimax method to perform triclustering analysis on microarray gene expression data. The $\delta$-Trimax method aims to find a tricluster that has a mean square residual smaller than $\delta$ and a maximum volume. Tricluster is obtained by deleting nodes from 3D data using multiple node deletion and single node deletion algorithms. The tricluster candidates that have been obtained are checked again by adding some previously deleted nodes using the node addition algorithm. In this research, the program improvement of the $\delta$-Trimax method was carried out and also the calculation of the resulting tricluster evaluation result. The $\delta$-Trimax method is implemented in two microarray gene expression data. The first implementation was carried out on gene expression data from the differentiation process of human-induced pluripotent stem cells (HiPSCs) from patients with heart disease, resulting in the best simulation when $\delta =0.0068$, $\lambda =1.2$, and obtained five tricluster, which are considered as characteristics of heart disease. The second implementation was implemented on HIV-1 data, best simulation when $\delta =0.0046$, $\lambda =1.25$ and produced three genes as biomarkers, with the gene names AGFG1, EGR1 and HLA-C. This gene group can be used by medical experts in providing further treatment.

DNA microarray technology is used to analyze thousands of gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Clustering algorithm has been used as an alternative approach to identify structures from gene expression data. In this paper, we introduce a transform technique based on Singular Value Decomposition (SVD) to identify normalized matrix of gene expression data followed by Partitioning Around Medoids (PAM) to cluster and then displaying the best cluster according to Davis Bouldin Index (DBI) based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard dataset demonstrated the effectiveness of the algorithm in gene expression data.

Alzheimer’s is a disease chronic neuro-degenerative progressive disorder in the adult human brain that tends to be old and which causes memory, thought and behavior problems and the most common causes of dementia that usually worsen over time. The microarray data of AD gene expression were taken from six different brain regions size 54675 id probes genes x 161 samples in 74 normal samples and 87 samples affected by Alzheimer’s. The development of microarray technology used in dataset genetic expression includes Alzheimer’s. To discover hidden pattern from microarray of Alzheimer’s gene expression data we propose finding correlated bicluster using FABIA biclustering. In this paper, we apply the analysis for bicluster acquisition (FABIA) factor technique for gene expression matrix data that has been standardized median centering and normalized, and then this multiplicative method produces two very sparse Laplacian variables with heavy-tailed non-Gaussian signals for the desired bicluster. To measure information on the bicluster we use I / NI. Evaluate the results of our cycle; we use the Jaccard index and the Munkres algorithm which implemented in the Truecluster in the open source R package. From the experimental results, there are nine biclusters formed by data’s AD that can be analyzed.