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Yuni Yulida
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Mathematics Department, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University. Jl. A. Yani KM.35.8 Banjarbaru, Kalimantan Selatan
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Epsilon: Jurnal Matematika Murni dan Terapan
Published by Universitas Lambung Mangkurat
ISSN : 19784422     EISSN : 26567660     DOI : http://dx.doi.org/10.20527
Core Subject : Science, Engineering,
Decision Sciences, Operations Research & Management Transportation
Jurnal Matematika Murni dan Terapan Epsilon is a mathematics journal which is devoted to research articles from all fields of pure and applied mathematics including 1. Mathematical Analysis 2. Applied Mathematics 3. Algebra 4. Statistics 5. Computational Mathematics
Arjuna Subject : Matematika - Matematika Terapan
Articles 6 Documents
 1 
Search results for , issue "Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1" : 6 Documents clear
MULTI OBJECTIVE FUZZY LINEAR PROGRAMMING Muhammad Mefta Eryshady; Oni Soesanto; Muhammad Ahsar Karim
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (167.233 KB) | DOI: 10.20527/epsilon.v8i1.104

Abstract

Linear programming is a general model that can be used in problem solving the allocation problem of limited resources optimally. The mathematics model of linear programming consists of two function: objective function and constraint function. Based on the number of objective functions, linear programming is divided into two types: Single Objective Linear Programming and Multi-Objective Linear Programming. Multi Objective Linear Programming which values are defined in the scope of fuzzy is called Multi Objective Fuzzy Linear Programming. To find the optimal solution of the problem, firstly it is divided into a linear program with single objective and solved using the simplex method. This research was carried out by using a literature study. The results of this study indicate that the optimal solution of Multi Objective Fuzzy Linear Programming will be decision variable ()x, that are: 12,,...,nxxx which its values if they are substituted into the constraint function, the results will be consistent with the limits of specified| resources, as well as if they are substituted into the objective function, then it will be obtained the optimal solution of all expected purposes.
PENENTUAN LOKASI TERBAIK LINGKUNGAN PERUMAHAN DI PERKOTAAN DENGAN PENDEKATAN FUZZY Siti Sulistiani; Oni Soesanto; Mohammad Mahfuzh Shiddiq
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (255.537 KB) | DOI: 10.20527/epsilon.v8i1.105

Abstract

Fuzzy logic provides a solution to the problem of uncertainty, because in real life often encountered things that are uncertain or vague and sometimes difficult to resolve firmly. Fuzzy logic is able to overcome the uncertainty in real-life cases. One is the problem of determining the right location to create a particular facility such as a residential neighborhood, where there are several factors to consider and sometimes difficult to decide firmly. To make a multi-criteria decision, there are several methods that can be used. But in this research that will be used is one method of Fuzzy Analytical Hierarchy Process (FAHP) is Method Extent Analysis Chang (1996). The purpose of this research is to determine optimal location of an urban housing environment by using Extent Analysis Chang Method. The decision-making process is carried out with the steps contained in Ext's Analysis (1996) method sequentially starting from criteria, sub criteria and alternatives. The final decision is made by ranking the normalized vector weighting of all criteria and sub criteria. Location A was chosen as the optimal location, because it has the highest ranking weighting that is 0.330.
JUMLAH ANTI IDEAL FUZZY DARI NEAR-RING Saman Abdurrahman
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (192.792 KB) | DOI: 10.20527/epsilon.v8i1.100

Abstract

In this paper, the concept of addition of fuzzy anti ideal from the near-ring and prove the properties of the sum. The result of this study is the ideal anti-fuzzy addition of the near-ring, is the ideal anti-fuzzy of the near-ring.
METODE KARMARKAR SEBAGAI ALTERNATIF PENYELESAIAN MASALAH PEMROGRAMAN LINEAR Bayu Prihandono; Meilyna Habibullah; Evi Noviani
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (337.667 KB) | DOI: 10.20527/epsilon.v8i1.101

Abstract

Linear programming is a tool for completing an activity plan that has been established in a mathematical model for the desired goal to be achieved. This study aims to introduce how to solve linear programming problems using Karmarkar method. In the Karmarkar method, the linear programming problem is written in a special form called the canonical form of Karmarkar. If there are standard linear programming problems will be solved by Karmarkar method, then the problem must first be converted into Karmarkar canonical form. How the Karmarkar method works starts from the determination of the starting point based on the number of variables, followed by the calculation of radius, the completion range, and the value of the termination criteria. Iterations on the Karmarkar method can be stopped if the value of the objective function has satisfied the condition less than the predefined stop criteria, so the optimum solution point has been obtained.
MODEL MATEMATIKA KOMENSALISME ANTARA DUA SPESIES DENGAN SUMBER TERBATAS Friska Erlina; Yuni Yulida; Faisal Faisal
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.634 KB) | DOI: 10.20527/epsilon.v8i1.102

Abstract

MODEL MATEMATIKA KOMENSALISME ANTARA DUA SPESIES DENGAN SUMBER TERBATAS
METODE TAGUCHI UNTUK PENINGKATAN KUALITAS MUTU PRODUK Akhriyandi Wijanarta; Nur Salam; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (123.893 KB) | DOI: 10.20527/epsilon.v8i1.103

Abstract

Costumers tend to choose a better product so that the quality improvement of a product is crucial. Quality control is a continuous process to ensure the quality of the products. The Taguchi method that was introduced by Dr. Genichi Taguchi in 1940 used to improve the quality of product and process as well as to reduce the production cost incurred by the company to minimize damage or defect in the products. The purpose of this research is to explain the procedures of Taguchi method to improve product quality. The results of the research show that the procedures using Taguchi method, are: the first step is counting the number of experiments and choosing the form of orthogonal arrays from the number of factors and levels that will be tested. The second step is conducting experiment and obtains data than calculating the mean value, and determining signal to noise rasio that is consistent to the quality characteristics of the experiment. The third step is analyzing experiment data using analysis of variance to determine factors that have a significant influence, then calculating the contribution value of each factor. If the contribution value of factor is smaller than the contribution value of error value then the factor will be pooling up. After getting the optimal alternative factors the fourth step is confirming experiment to examine the conclusion of the obtained data experiment. Furthermore, the five step is calculating the confidence intervals of response mean value betwen the prediction result of Taguchi method and the result of confirming experiment. After that, the sixth step is calculating Taguchi loss function to determine the amount of damage cost spent to improve the quality of product.

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