The Effect of Practicum Tools with Differentiated Learning Strategies on Student Learning Outcomes

This study aims to determine the effect of using practicum tools with diferentiated learning strategies on student learning outcomes. The research method used is quasi-experimental design. The population in this study was all of class X MIA, totaling 212 students, of SMA Negeri 8 Medan. The research sampling technique is purposive sampling technique. Non-equivalent control group design is the type of the research design. The sample in the study consisted of an experimental class, namely class X MIA 6, and a control class, namely class X MIA 5. The experimental class was treated with differentiated learning strategies, while the control class was treated with conventional learning. The research instruments used were multiple-choice questions and student observation sheets. The average score of the posttest for the experimental class was 76.28 with a standard deviation of 6.9, and that of the control class was 72.08 with a standard deviation of 6.7. The pretest and posttest data obtained have been analyzed and met the prerequisites for testing the hypothesis, namely, the data is normally distributed and is homogeneous. The result of the right-tailed t-test on the hypothesis obtained the value of tcount > ttable, namely 2.608 > 2.002 whit = 0.05. Based on the testing criteria, the accepted hypothesis is H, which means that there is an effect of the treatment that is the use of practicum tool with different learning strategies on student learning outcomes at SMA Negeri 8 Medan for the 2022/2023 academic year. The result of the simple linear regression equation is Y = 65.9 + 0.34X

A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches

Assembly optimisation activities occur across development and production stages of manufacturing goods. Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) problems are among the assembly optimisation. Both of these activities are classified as NP-hard. Several soft computing approaches using different techniques have been developed to solve ASP and ALB. Although these approaches do not guarantee the optimum solution, they have been successfully applied in many ASP and ALB optimisation works. This paper reported the survey on research in ASP and ALB that use soft computing approaches for the past 10years. To be more specific, only Simple Assembly Line Balancing Problem (SALBP) is considered for ALB. The survey shows that three soft computing algorithms that frequently used to solve ASP and ALB are Genetic Algorithm, Ant Colony Optimisation and Particle Swarm Optimisation. Meanwhile, the research in ASP and ALB is also progressing to the next level by integration of assembly optimisation activities across product development stages