Design and verification of Cov-trek prototype based on systems modeling language (SysML) and modeling framework

In the software development process, requirements are an essential but often overlooked step. Comprehensive requirements management can help developers work on the system to meet project requirements and play a vital role in communication among stakeholders. Using natural language to describe and record user needs results in ambiguity, inconsistency, inaccuracy, and incompleteness. This research uses a model-based approach using SysML to support integrated software development to improve modeling process and requirement analysis accuracy. Integrate the process of determining system requirements in software design using the SysML modeling language. Cov-trek was chosen as a case study for the exploration of SysML support in the software development process. The Cov-trek is an application designed to track a person's movements to mitigate the spread of COVID-19. SysML is used to accomplish this work. A subset of SysML, an intermediate modeling language, is utilized to ensure a progressive transformation that system stakeholders can understand. By using SysML, the main requirements of the application can be modeled with requirements diagrams and validated in the design. © 2023 Author(s).

Classifying the Students' Behavior on e-Learning System using Fine-Tuning K-NN Method

monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split. © 2022 IEEE.