Online learning has been widely applied due to developments in information technology. However, there has no evaluation and application for primary school students. All innovation efforts in learning are directed at improving the quality of education by creating an active learning atmosphere for students. Students’ participation in the teaching-learning process can be improved by selecting appropriate learning materials suitable to the student’s learning style. The research aims to develop and measure the impact of an Artificial-Intelligence (AI)-based learning style prediction model in an online learning portal for primary school students. The subjects were recruited from Indonesian primary school students in grades 4 to 6. To fulfill the principle of personalized learning, the AI model in the online learning portal was designed to recommend learning materials that suit students’ learning styles. We formulated a new AI approach that enables collaborative filtering-based AI models to be driven by learning style prediction.With this AI algorithm, the online learning portal can provide material recommendations tailored specifically to the learning style of each student. The AI model performance test achieved satisfactory results, with an average RMSE (Root Mean Squared Error) of 0.9035 from a rating scale of 1 to 5. Moreover, students’ learning performance was improved based on the results of t-test analysis on 269 subjects between the pre-test and post-test scores.

Background Several reports on the discovery of SARS-CoV-2 mutations and variations in Indonesia COVID-19 cases led to genomic dysregulation with the first pandemic cases in Wuhan, China. MicroRNA (miRNA) plays an important role in this genetic regulation and contributes to the enhancement of viral RNA binding through the host mRNA. Objective This research is aimed to detect miRNA targets of SARS-CoV-2 and examines their role in Indonesia cases against Wuhan cases. Methods SARS-CoV-2 sequences were obtained from GISAID (https://www.gisaid.org/), NCBI (https://ncbi.nlm.nih.gov), and National Genomics Data Center (https://bigd.big.ac.cn/gwh/) databases. MiRDB (https://github.com/gbnegrini/mirdb-custom-target-search) was used to annotate and predict target human mature miRNAs. For statistical analysis, we utilized a series chi-square test to obtain significant miRNA. DIANA-miRPath v3.0 (http://www.microrna.gr/miRPathv3) analyzed the Gene Ontology of mature miRNAs. Result The statistical results detected five significant miRNAs. Two miRNAs: hsa-miR-4778-5p and hsa-miR-4531 were consistently found in the majority of Wuhan samples, while they were only found in less than half of the Indonesia samples. The other three miRNA, hsa-miR-6844, hsa-miR-627-5p, and hsa-miR-3674, were discovered in most samples in both groups but with a significant difference ratio. Among these five significant miRNA targets, hsa-miR-6844 is the only miRNA that has an association with the ORF1ab gene of SARS-CoV-2. Conclusion The Gene Ontology analysis of five significant miRNA targets indicates a significant role in inflammation and the immune system. The specific detection of host miRNAs in this study shows that there are differences in the characteristics of SARS-CoV-2 between Indonesia and Wuhan.

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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