ABSTRACT
One critical problem in Indonesia's national joint courses program, initiated by the ministry of education and culture of Indonesia, is the lecturer-course assignment problem. Although the lecturer-course assignmentproblem has been studied widely as part of the education timetabling problem, no existing lecturer-courseassignment model suits this circumstance. The new cases in this program are as follows. First, this program isconducted online. Second, the participants are students and lecturers from different universities. Based on thisproblem, this work proposes a novel lecturer-course assignment model that suits this program. The lecturers'preferred courses and timeslots become hard constraints. The model has three objectives: (1) maximizing theeducational quality, (2) maximizing the lecturers' time preference, and (3) minimizing the number of unservedclasses. This model is developed by using integer linear programming and optimized by using cloud theory-basedsimulated annealing. This proposed model is then compared with the four previous lecturers-course assignmentmodels. The first model concerns about minimizing the number of unserved classes, while the second model focuseson maximizing the education quality. The maximum number of classes per course for every lecturer is considered inthe third model while balancing the lecturer’s load (teach, research, community service) is the feature of the fourthmodel. The research concludes that the proposed model is appropriate for lecture-course assignment in Indonesia’snational joint courses program compared to the previous models. Based on the simulation result, the proposed modelperforms moderately in education quality and several unserved classes. Meanwhile, the proposed model is the best inthe timeslot preference aspect by creating a 25% to 28% higher total timeslot score than other previous models © 2022, International Journal of Intelligent Engineering and Systems. All Rights Reserved.
ABSTRACT
Since early 2020, the COVID-19 outbreak and its spread across the globe have disrupted many sectors. In Indonesia the COVID-19 have caused a severe impact on the economic, social, and political. Various actions were considered as preventive measures to slow down the virus propagation. This study aimed to investigate the relationship between attitude, knowledge, risk perception, information exposure and preventive intention behavior toward COVID-19 in Indonesia. An empirical quantitative study was conducted using data collected in March and April 2020. The sample consisted of 214 respondents in Indonesia through online survey questionnaires based on convenience sampling methods. The regression analysis results showed that 42% of the variance in behavioral change on the infection was explained by the dependent variables. Attitude had a strong positive relationship with intention behavior with (β = .37, p = .000). This study found that intention behavior was elicited by attitude (β = .37, p = .000), information exposure (β = .10, p = .01), and risk perception (β=.29, p = .000). However, knowledge did not influence preventive intention behavior (β=.00, p = .94). These findings contribute towards preventive intention literature to support practitioners, public health authorities, health care policymakers, and the government to shape effective prevention communication. Copyright © Behavioral Science Research Institute