Azra Sabrina
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Analisis Studi Literatur Pengembangan Media Berbasis Algoritma Pemrograman Dalam Pembelajaran Matematika Azra Sabrina; Yahfizham Yahfizham
Jurnal Elektronika dan Teknik Informatika TerapanĀ ( JENTIKĀ ) Vol. 1 No. 4 (2023): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v1i4.496

Abstract

A multitude of software and media have been made possible by technological advancements, which may be utilized to produce more engaging and successful mathematics learning experiences. By using algorithms and programming languages, the software can run well. Reviewing educational materials that may be utilized to advance mathematical learning, together with their impacts and difficulties, is the goal of this study. This piece employs the technique of Systematic Literature Review (SLR). This SLR research aims to find, study, and evaluate comparable research to answer research questions. The development of mathematical learning through the use of learning materials based on algorithms according to previous research most people use geogebra and matlab. This study solely addresses the application of algorithm-based media programming in the advancement of mathematical learning, in accordance with earlier publications. It is anticipated that future academics will be able to advance mathematics learning using the algorithm-based media of this programming.
Systematic Literature Review: Implementasi Metode Big M dalam Mengoptimalkan berbagai Kasus Program Linier Azra Sabrina; Nursania Simbolon; Armina Rangkuti; Siti Salamah Br Ginting
Katalis Pendidikan : Jurnal Ilmu Pendidikan dan Matematika Vol. 2 No. 3 (2025): Katalis Pendidikan : Jurnal Ilmu Pendidikan dan Matematika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/katalis.v2i3.2018

Abstract

Linear programming is a crucial operations research method for optimization problems in various fields like manufacturing and logistics, but it is often hindered by the absence of a basic feasible solution due to artificial variables. The Big M method addresses this by adding artificial variables and a large penalty, allowing the discovery of a valid initial solution without altering the constraint structure. This research employs a Systematic Literature Review (SLR) to examine the implementation of the Big M method in Indonesia from 2015-2025, analyzing journals and proceedings related to linear programming optimization. The review findings indicate that this method is highly effective and flexible for diverse cases such as production, animal feed, and scheduling, capable of handling artificial constraints and optimizing profit or cost. Although efficient, the Big M method has drawbacks, including computational complexity and dependence on the precise selection of the M value, which can affect result accuracy. Overall, the Big M Method remains relevant and important for real-world optimization problems requiring systematic constraint handling.