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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 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2" : 6 Documents clear
IDEAL FUZZY NEAR-RING Saman Abdurrahman; Na'imah Hijriati; Thresye Thresye
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (314.346 KB) | DOI: 10.20527/epsilon.v6i2.83

Abstract

In this paper will be discussed ideal near-ring, ideal fuzzy near-ring covering the relationship between ideal near-ring and ideal fuzzy near-ring.
IDEAL DIFERENSIAL DAN HOMOMORFISMA DIFERENSIAL Na'imah Hijriati; Saman Abdurrahman; Thresye Thresye
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.921 KB) | DOI: 10.20527/epsilon.v6i2.84

Abstract

Ideal differential is the ideal of differential ring that satisfies if for each a  I, and every   ,  (a)  I, whereas the differential homomorphism is a commutative homomorphism of rings against each derivation. This paper is presented the properties of differential ideal and differential homomorphism.
PELABELAN GRACEFUL, SKOLEM GRACEFUL DAN PELABELAN ???? PADA GRAF H-BINTANG DAN A-BINTANG Nurul Huda; Zulfi Amri
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.48 KB) | DOI: 10.20527/epsilon.v6i2.85

Abstract

The graph G = (V, E) is the ordered set of sets in which V is the set null node and E is a set of arcs. Labeling on graph G is determination of node and arc values or both with certain rules. Labeling graceful is the α α function of the set of vertices V to the set of numbers 0.1,2, .... ???? which induces the α 's bijtive function of the set of arc E to the set number 1,2, .... ???? where each arc uv ∈ E with node u, v ∈ V apply α '(uv) = α (????) - α (????). The graceful grid labeling is a modification of graceful labeling ie the injection function μ from the set of vertices V to the set of numbers 1,2, .... ???? yang induces the μj 's bijtive function of the arc set E to the set of numbers 1,2, .... ???? where each arc uv ∈ E with node u, v ∈ V apply μ '(uv) = μ (????) - μ (????). Labeling ρ is another modification of graceful labeling that is the γ injection function of the set of vertices V to the set of numbers 0.1, 2, .... ???? + 1 which induces the function bitif γ 'from the set of arc E to set of numbers 1,2, .... ???? where each arc uv ∈ E with node u, v ∈ V apply γ '(uv) = γ (????) - γ (????). The H-star chart is formed of the letter H and all its leaves are given a star graph ????????. A-star chart formed from letter A and all its leaves are given a star graph ????????. In this paper is given graceful label construction, graceful scheme and labeling ρ for H-star graphs A-star.
PENERAPAN TEORI KENDALI PADA MASALAH INVENTORI Pardi Affandi; Faisal Faisal; Yuni Yulida
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.869 KB) | DOI: 10.20527/epsilon.v6i2.86

Abstract

This paper will examine the application of Control Theory to the problem Inventory, will be developed the first model in which dynamic demand and inventory available all the time. The discussion focused on inventory system analysis nonlinear-shaped production and production costs are treated as a function each inventory level and production level. Then expanded the model first to the next model where the decline in goods is taken into account. Level damage is calculated as a function of time with the amount already available. For both models, optimal control theory will be used to obtain policy optimal control, to obtain optimal results.
ESTIMASI MODEL LINEAR PARSIAL DENGAN PENDEKATAN KUADRAT TERKECIL DAN SIMULASINYA MENGGUNAKAN PROGRAM S-PLUS Nur Salam; Dewi Sri Susanti; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.605 KB) | DOI: 10.20527/epsilon.v6i2.82

Abstract

Partial linear model (model semiparametric) is a new approach in the regressionmodels between the two regression models are already popular parametric regression andnonparametric regression. Partial linear model is a model that includes both thecombination of parametric components and nonparametric components. This study usesliterature by studying semiparametric regression analysis, finding and determining theestimated parameters. Partial linear model has the form: : ???????? = ???????????????? + g(????????)+ ???????? with???????? and ???????? are explanatory variables, g (.) is an unknown function (smooth function), β isthe parameter of unknown function, ???????? response variable and ???????? is an error with the mean(????????) = 0 and variance ????????2 = ????(????????2).The results showed that the partial linear model parameter estimation canbe performed using the least squares method in which part of the linear model usingnonparametric kernel approach and subsequent estimation results are substituted into thepartial linear model to estimate the parametric part of the model by using the linear leastsquares method. Results obtained partial linear estimation is ???? ???? (t) = ????????????????????=1 (Yi - ???????????? +???????? ) dengan ???????? = (???? ???? ???? )−???? ???? ???? ???? .Based on the simulation results obtained output values and graphs are for theparametric, graphical display and qqline qqnorm estimator beta (β) is (????) yaitu ????0, ????1and ????2 can be seen clearly, where if n is greater (n → ∞) and the greater replicationiteration r , then the points are spread around the more straight line and a straight line.This indicates the greater n and r, the beta (β) closer to the normal distribution.Nonparametric estimator simulation results in this section are taken as an example of anormal kernel function values approaching g (T). So it can be concluded briefly that if thelarger n (n → ∞), the estimator of the nonparametric part closer to the partial linearmodel g (T).
ALGORITMA GENETIKA PADA PENYELESAIAN AKAR PERSAMAAN SEBUAH FUNGSI Akhmad Yusuf; Oni Soesanto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v6i2.87

Abstract

The Genetic Algorithm is one approach to determining global optimum that is based on the theory of evolution. Outline the steps in this procedure starting with establishing a set of potential solutions and making changes with some iterations with genetic algorithms to get the best solution. Calculation the root of a function is actually a classic problem in mathematics. For that, various methods have been numerically developed. From the results of the implementation of genetic algorithm to find the root of the equation of a function h (x1, x2) = 1000 (x1-2x2) 2+ (1-x1) 2 in can be that FitMax (genome 9) = 10, FitMin (genome 107) = 0, FitAvr = 0.153, FitTot = 30.6, Best Genome: 10011001001000110010, x1 = 1 and x2 = 0.5 and this is the same as the exact value or value actually from the root of the equation

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