Optimization Of Learning Scheduling Using Linear Regression

Authors

  • Naufal Falaah Nduru STMIK Kaputama Binjai, Indonesia
  • Akim M.H Pardede STMIK Kaputama Binjai, Indonesia
  • Anton Sihombing STMIK Kaputama Binjai, Indonesia

DOI:

https://doi.org/10.55227/ijhet.v1i3.66

Keywords:

Optimization, Learning Scheduling, Linear Regression

Abstract

Scheduling is a process, method, manufacture, or schedule to be included in the schedule (KBBI). Scheduling that is needed in general and that occurs at one point of education in making study schedules requires various supporting factors from the division of teacher tasks. Based on this, efforts to facilitate learning schedules are made to optimize an activity so as to increase productivity and effectiveness.To carry out learning activities that are carried out conventionally and for a long time, teachers who have teaching schedules in two or more different classrooms at the same time must be revised by changing their respective schedules or reducing the hours that occur as guided by the teacher. At SD S Muhammadiyah Sambirejo, which is a school located in Sambirejo Village, Binjai District, Langkat Regency, and has several educators and educational staff at this school, I focus on the case study of learning scheduling, so that teaching and learning activities in a school must pay attention to the learning schedule and avoid clashes. This makes it easier to prepare a study schedule. The method used is linear regression as an optimization of learning scheduling. The results of testing the linear regression method in optimizing learning scheduling using linear regression with a total of 10 forecasting data as analysis and determining the forecasting result value of 2.0063334474495 obtained data results with the number of conflicting hours.

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References

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Published

2024-07-29

How to Cite

Naufal Falaah Nduru, Akim M.H Pardede, & Anton Sihombing. (2024). Optimization Of Learning Scheduling Using Linear Regression. International Journal of Health Engineering and Technology (IJHET), 1(3). https://doi.org/10.55227/ijhet.v1i3.66