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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Muhammad Irfan
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

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Implementation of Genetic Algorithm for Subject Scheduling at SD Taman Cahya Pematangsiantar Muhammad Irfan; Muhammad Ridwan Lubis; Zulaini Masruro Nasution
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (707.227 KB) | DOI: 10.55123/jomlai.v1i2.940

Abstract

The teaching schedule in schools is essential in teaching and learning activities; this schedule aims to support, facilitate, and improve the quality of education. With a teaching schedule, teaching and learning activities will run smoothly and efficiently. Until now, the scheduling of lessons in several schools is still done conventionally by the curriculum department, with previously held meetings for the division of tasks with the supervising teacher. The conventional teaching scheduling system for school teachers will be deemed less effective. In addition to requiring very high accuracy and relatively few estimates, this method also allows for errors. Therefore, this study aims to implement a genetic algorithm to optimize subject scheduling and apply the teacher scheduling model generated by the genetic algorithm in a web application. The data of this study were collected from observations at SD Taman Cahya Pematangsiantar. As a result, scheduling using genetic algorithms can generate schedules automatically, displaying the plan on the day and hour of each teacher's teaching schedule, thus creating an optimal solution for scheduling. In addition, applying the Genetic Algorithm is faster and easier in the process of making a schedule for setting teacher teaching hours so that it does not take a long time.
Co-Authors Muhammad Ridwan Lubis Zulaini Masruro Nasution