This study aims to systematically examine the use of Math Mechanixs software as a medium for learning mathematics to improve students' computational skills. The method used is Systematic Literature Review (SLR), with data sources obtained from various scientific databases such as Google Scholar, Garuda, DOAJ, and ResearchGate. The study was conducted by filtering articles based on inclusion and exclusion criteria, then analyzed to find patterns and effectiveness of the use of the software. The results of the review indicate that Math Mechanixs, with its 2D/3D graphic visualization features, equation editor, and scientific calculator, has great potential in helping students understand mathematical concepts more efficiently and interactively. In addition to increasing the speed and accuracy of calculations, this software also encourages the development of students' algorithmic and logical thinking skills. However, more empirical research is needed to strengthen these findings in the context of Indonesian education. This study recommends the integration of Math Mechanixs in mathematics learning with the support of teacher training and adequate supporting facilities.
 
 

Efficient resource allocation is a major challenge in various sectors, especially when faced with limitations in quantity, time, and cost. This study aims to examine the application of linear programming as an optimization method in solving assignment problems, where a number of resources must be optimally allocated to a number of specific tasks. Through a literature study approach, this study examines various relevant previous study results, especially in the Indonesian context. The results of the study indicate that linear programming is able to improve operational efficiency, reduce costs, and produce a more balanced task distribution. However, this model has limitations in dealing with non-linear conditions and data uncertainty, so integration with other methods such as fuzzy logic or genetic algorithms is needed. This study is expected to broaden the understanding of the benefits of linear programming and encourage its wider application in quantitative-based decision making.