The COVID-19 pandemic led to the implementation of the Work From Home<strong> </strong>(WFH) policy. The impact of this policy is to replace the process of teaching and learning activities by using an online learning system. In this industrial era 4.0, the integration of web applications is very much needed in problem-solving learning. One that can be used to integrate web applications in learning through project-based learning. The focus of this research is to study the effects of online project-based learning applications on mathematics students' visual thinking continuum. Instruments used in the form of rubric virtual mind maps, audio-visual and virtual posters are used to observe students' visual thinking continuum. The instrument was analyzed with the Rasch measurement model software named WINSTEPS. This study used one group pretest-posttest design to see the effect of integrating web applications in learning through project-based learning analyzed using SPSS. The results of instrument analysis for rubric virtual mind maps, audio-visual products, and virtual posters on measure person obtained good person reliability, MNSQ infit, and MNSQ OUTFIT the average rating is very good. For the ZSTD infit and the ZSTD outfit, the average value is close to 0.0 so that the quality of the person's reliability is good. The results of the conformity quality of the items with the model studied from the fit order items obtained the value of Outfit mean square (MNSQ), Outfit Z-standard (ZSTD), and the value of Point Measure Correlation (Pt Mean Corr) for the three instruments met good criteria. The results of the analysis of paired samples test from 54 respondents obtained p <0.05 so that the base learning project has the effect of increasing students' visual thinking continuum. Implementation of online project-based learning in mathematics learning is an alternative that can be used to improve students' creative problem-solving skills in online learning
An Integration of “Online Interactive Apps” for learning can be a disruptive learning innovation in higher education. The material of the application of graph theory course has application topics in the real world. How students use the application can be used as a measure of creative problem solving. Mixed method was used in the study. The first phase of the study used one group non-randomized design pre-test and post-test. Participants were students who took courses in the application of graph theory. The second phase of the study involved an exploration of creative problem solving performance through qualitative interviews. Analysis of problem solving skills with creative problem solving instruments adapted from the CPS version 6. The average performance of students is very good in exploring data and building acceptance, performance is good in generating ideas and designing processes. Finding from this research is a significant improvement of students' Creative Problem-Solving by integrating learning the application of graph theory with “Online Interactive Apps”. The results obtained that 69% of student opinions strongly agree on learning activity can improve creative problem solving. The implementation of “Online Interactive Apps” can be used as an alternative to disruptive learning innovation in mathematics teaching-learning.
Vehicle Routing Problem (VRP) has an important role in logistics distribution from the depot to the customer, to get the minimum cost delivery route. To get optimal results, it is necessary to improve route from the initial solution. Variable Neighbourhood Descent (VND) is one of the metaheuristics that examine of a number of neighbourhood operators to get the optimal route. A VRP route is called optimal if there are no other routes that can be generated from all the neighbourhood operators used in VND. This article describes the application of VND to get the optimal route on CVRP, MDVRP, and VRPTW. The results of the experiment on some test data used indicate that VND can be used to get more optimal length and travel time route.
One of learning alternatives that can be applied to improve students’ activity and creativity is e-learning.. Implementation of e-learning for applied graph theory course used e-module for course material, e-portfolio for student assignments, and online-based tasks that can be accessed by other students, hence it is could be as a pioneer of wider implementation of online learning. In applied graph theory course, students needed to have direct experience in solving problems in related industries/institutions. From direct application experience in the field, students raise problems, model problems in the graph, and determine appropriate methods to solve the problem. Creative thinking process could be identified from students’ problem solving task by using Creative Problem Solving (CPS) model. There are four indicators of main step (understanding the challenge, generating ideas, preparing for action, and planning approach) and eight minor step indicators (constructing opportunities, exploring data, problem framing, generating ideas, developing solutions, building acceptance, appraising tasks, and designing process) in the recent version. Purpose of the paper is to analyze result of CPS model implementation, and to discuss student’s responses to the implementation. By implementing the CPS model through e-learning, it could be identified that student’ s creativity in real problem solving were good. The questionnaire indicated that the student response strongly agree with the implementation of this learning to develop creativity.
Multi Depot Vehicle Routing Problem (MDVRP) is one of Vehicle Routing Problem (VRP) variants. MDVRP is a VRP that uses more than one depot for the distribution process. In this paper, the MDVRP will be solved using the Grey Wolf Optimizer (GWO). This algorithm is inspired by hunting technique and hierarchy of grey wolves. The completion of MDVRP consists of two stages, they are grouping and routing. In the grouping stage, customers are grouped to nearest depot. In the routing stage, the route is determined at each depot using “route-first, cluster-second”. In the route-first phase a giant tour is formed by forming population array. In the cluster-second phase, a split will be conducted on the giant tour, so that a number of routes are formed with the total demand for each route does not exceed the vehicle capacity. The implementation of GWO algorithm for MDVRP was designed in Borland Delphi 7.0 programming language. The algorithm was tested using 2 depots and 9 customers formed 2 routes at each depot with a total distance of 833 km. Another test was carried out by comparing the GWO with the ACO using the Cordeau dataset. GWO gives the best result (shortest distance) than ACO.
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