Penguatan Keterampilan Guru dalam Pemanfaatan GeoGebra sebagai Media Pembelajaran Program Linear

To support students facing rapid technological development, teachers must provide real experience in the learning process. Linear programming materials that require visualization in their understanding can be taught by integrating GeoGebra software. This community service aims to strengthen teacher skills in using GeoGebra for linear programming learning media. Community service is carried out through training and mentoring with an active learning participant approach. The activities are carried out through three core stages: teacher training in mastering the GeoGebra interface, assistance in visualizing linear programming problems, and aid in solving linear programs. Although most of the participants stated that GeoGebra could help learn linear programming in the classroom, its implementation needs to consider various aspects, such as the preparation of learning tools and equipment, learning time, and student readiness

Analysis of the Level of Understanding of the Concept of the Isoline Method of Linear Program Material Based on APOS Theory in terms of Learning Interest

One of the methods that can be used to find solutions to linear programming models is the Graphical Method. Graphical methods are divided into the isoline method and the extreme point method. Based on initial observations and interviews, it was found that around 54% of students still needed help understanding the isoline method material in solving linear programs, especially when given real problems related to everyday life. In addition, students' learning interest in linear programming courses also varies. So the purpose of research in this article is to describe the level of understanding of students who have high, medium, and low learning interest in linear programming material, precisely the isoline method material, in determining the optimum solution. The method in this study is a qualitative method with a sampling technique using purposive sampling. Data collection was carried out using a questionnaire method for data on student learning interests and semi-structured test and interview methods to describe the level of student understanding based on APOS theory. The instruments in this study were questionnaires, written tests, and interview guidelines. Checking the validity of the data in this study used time triangulation. The collected data was analyzed using descriptive qualitative analysis. The results of the research show that: (1) subjects with high learning interest are at the level of schematic understanding; (2) subjects with interest in learning are at the level of understanding objects; and (3) subjects with low interest in learning are at the level of understanding of Action

A Decision Aid to Help Farmers Find the Minimum Cost Fertilizer Combination

Fertilizer recommendations are typically given as the pounds of nitrogen, phosphorus, and potassium needed per acre. Many crop budgets also list the fertilizer costs in terms of the N, P, and K. This can cause a problem for farmers as they must purchase their fertilizer in products that in some cases contain multiple nutrients. These multinutrient fertilizers make it difficult or impossible to calculate a price per unit of a nutrient. Thus, finding the minimum cost fertilizer combination by using a price per nutrient approach may not work. This paper shows how a linear programming approach will always give the minimum cost fertilizer combination and then shows how a decision aid is developed in Excel. The developed decision aid has many uses in extension services where farmers have little understanding of linear programming. The decision aid is also useful in helping farm management students learn about linear programming

Sensitivity Analysis as a Managerial Decision Making Tool

Decision making is an integral part of operations management. It may be useful to a decision maker to have some indication of how sensitive an alternative choice might be to the changes in one or more of those values. Unfortunately, it is not possible to explore all the possible combinations of all the variables in a typical problem. In spite of this, there are some elements that a decision maker can use to assess the sensitivity of assumption probabilities. One of the tools useful for the analysis in some decision making problems is sensitivity analysis. It provides a range of feasibility over which the choice of alternative remains the same. Successful decision making consists of several steps, the first and most important being carefully defining the problem. Given that linear problems can be extensive and complex, they are solved by using sophisticated computer methods. This paper will present software solutions available for personal computers (Lindo, POM). For a manager taking the decision, however, a solution model is only part of the answer. Sensitivity analysis offers a better understanding of the problem, different effects of limitations and “what if“ questions. The insights obtained are frequently much more valuable that a specific numerical answer. One of the advantages of linear programming lies in the fact that it provides rich information on sensitivity analysis as a direct part of the solution.feasibility range, linear programming, Lindo, POM, optimum solution, optimum range, sensitivity analysis.

ANALISIS KEMAMPUAN PEMECAHAN MASALAH MATEMATIS SISWA SMK PADA MATERI PROGRAM LINIER

The purpose of this research is to describe, document, analyze, and interpret errors made by students in solving mathematical problem solving ability test questions on linear programming material. The approach and method used in this study used a qualitative approach in the descriptive type with 20 students of class XI SMK in the city of Bekasi as the research sample selected using purposive sampling technique. The instrument used in this research is a written test instrument for mathematical problem solving skills with linear programming material in the form of two description questions. The data analysis technique used in this study consisted of data reduction, data presentation, and inference. The indicators used are indicators according to Polya, namely: understanding the problem, making a settlement plan, implementing the plan, and checking the results of the settlement. The results of this study indicate that errors in understanding the problem are the most common and frequently experienced errors, with a percentage of 52%. Errors in making a resolution plan with a percentage of 42%. The error in implementing the plan is with a percentage of 28%. Finally, the error checking the results of the settlement obtained is with a percentage of 26%. The errors made by students are mostly on indicators of understanding the problem. Students are less able to understand the questions on linear programming material so that students experience many errors in planning problem solving

Problem Based Learning Technique and its effect on Acquisition of Linear Programming Skills by Secondary School Students in Kenya

The topic Linear Programming is included in the compulsory Kenyan secondary school mathematics curriculum at form four. The topic provides skills for determining best outcomes in a given mathematical model involving some linear relationship. This technique has found application in business, economics as well as various engineering fields. Yet many Kenyan secondary schools hardly teach the topic. The methods used are found to be difficult to execute leading to lack of understanding by the learners. The purpose of this study was to investigate whether Problem Based Learning intervention can encourage and improve students’ learning of linear programming. Students’ performance on an achievement test, and acquisition of linear programming skills were monitored. The study adopted the pre-test, post-test non-equivalent groups experimental design. The experimental group was taught the topic using the origin test and extreme points technique, a version of Problem Based Learning. The control group was taught using conventional methods. A mathematics achievement pre-test and a post-test were given to both groups to ascertain their respective entry and final performance abilities. Analysis of the post-test results was done using the means, standard deviations and paired samples t-test. In addition, item 3 of the mathematics achievement post-test, was used to determine the level of students’ acquisition of linear programming skills. The face and content validity of the research instruments were determined with the help of mathematic educators and experienced secondary school mathematic teachers. The split half method was utilized in determining the instruments’ reliability acceptable at reliability indices of 0.6. Pearson's coefficient (r) obtained for MAT 1 School 1 was 0.9211 and 0.9131 for school 2. Pearson's coefficient (r) for MAT 2 for School 1 was 0.8786 and for school 2 it was 0.8896.  Stratified Random Sampling was used to select ten each of form four boys, girls, and co-educational schools for the study. In total 1,502 form four students participated in the study. Of this total, 745 students formed the experimental while 757 formed the control group. Keywords: Problem Based Learning Technique, Linear Programming Skills, Pre-requisite knowledge,  Secondary   School Students, Keny

The C# implementation of numerical calculation process on non linear equation solutions

Solutions in the process of resolving numerical methods, especially "Solutions of Non Linear Equations", are determined by several criteria including: 1. Solutions will be obtained if the tolerances found from numerical calculations are less than or equal to the specified tolerances, depending on the criteria of the question. 2. The solution will be obtained if the function value f(xn +1) reaches zero (0). 3. The solution will be obtained if the value of xn+1 has a constant value (no change). In some observations of manual efforts to find non-linear equations solutions, students cannot do the calculation perfectly. This is due to lack of understanding of teaching materials or modules and also the algorithms that are difficult to grasp. Moreover, the calculation has to be done for several times iterations in order to obtain the expected results. Comparing the results of calculations done manually using Microsoft Excel and the result of calculations using numerical applications created using C# programming language, it concludes: non-linear equations manual calculation to get one of the roots of non-linear functions tend to not completed in a short time so the error rate and accuracy cannot be measured. The calculations performed using Microsoft Excel, obtained the error rate reaches 0.158% and the accuracy rate reaches 99.842%. Calculations performed using numerical applications using the C# programming language obtained error rate reached 0.004% and accuracy rate reached 99.996%