Journal of Research in Mathematics Trends and Technology

Monte Carlo Simulation Approach to Determine the Optimal Solution of Probabilistic Supply Cost

2020-03-02T12:02:13+07:00

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probability
distribution from data variable collected based on historical data. The used model is probabilistic Economic Order Quantity Model (EOQ). This model then assumed use Monte Carlo simulation, so that obtained the total of optimal supply cost in the future. Based on data processing, the result of probabilistic EOQ is $486128,19. After simulation using Monte Carlo simulation where the demand data follows normal distribution and it is obtained the total of supply cost is $46116,05 in 23 months later. Whereas the demand data uses Weibull distribution is obtained the total of supply stock is $482301,76. So that, Monte Carlo simulation can calculate the total of optimal supply in the future based on historical demand data.

Existence of Polynomial Combinatorics Graph Solution

2020-03-02T12:06:18+07:00

The Polynomial Combinatorics comes from optimization problem combinatorial in form the nonlinear and integer programming. This paper present a condition such that the polynomial combinatorics has solution. Existence of optimum value will be found by restriction of decision variable and properties of feasible solution set or polyhedra.

Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach

2020-03-02T12:30:54+07:00

A heteroskedastic semiparametric regression model consists of two main
components, i.e. parametric component and nonparametric component. The model assumes
that any data (x̰ i′ , t i , y i ) follows y i = x̰ i′ β̰+ f(t i ) + σ i ε i , where i = 1,2, … , n , x̰ i′ = (1, x i1 , x i2 , … , x ir ) and t i
is the predictor variable. Parameter vector β̰ = (β 1 , β 2 , … , β r ) ′ ∈ ℜ r is unknown and f(t i ) is also unknown and is assumed to be in interval of C[0,π] .
Random error ε i is independent on zero mean and variance
σ 2 . Estimation of the
heteroskedastic semiparametric regression model was conducted to evaluate the parametric
and nonparametric components. The nonparametric component f(t i ) regression was
approximated by Fourier series F(t) = bt + 1
2 α 0 + ∑ α k 𝑐 𝑜𝑠 kt Kk=1 . The estimation was
obtained by means of Weighted Penalized Least Square (WPLS): min f∈C(0,π) {n −1 (y̰− Xβ̰−
f̰) ′ W −1 (y̰− Xβ̰− f̰) + λ ∫ 2
π [f ′′ (t)] 2 dt π
0 } . The WPLS solution provided nonparametric
component f̰̂ λ (t) = M(λ)y̰ ∗ for a matrix M(λ) and parametric component β̰̂ = [X ′ T(λ)X] −1 X ′ T(λ)y̰

Comparison of Rainfall Forecasting in Simple Moving Average (SMA) and Weighted Moving Average (WMA) Methods (Case Study at Village of Gampong Blang Bintang, Big Aceh District-Sumatera-Indonesia

2020-03-02T12:35:42+07:00

The changing climate causes rainfall to vary from period to period. This change has an impact on society, especially in agriculture such as crop failure. This study aims to predict rainfall in 2018 and 2019 with the Simple Moving Average (SMA) method and the Weighted Moving Average (WMA) method. Based on 2004-2018 data, the dry season occurs in February-October and the rainy season in November-January. The level of validation of forecasters in 2018 according to each the SMA method and the WMA method were 43.43% and 40.69%, respectively. Both of these methods are low and reasonable or acceptable. Based on the SMA method, the division of the dry season in 2019 is estimated in February-October while the distribution of the rainy season in the same year is in December-January. Based on the WMA Method that the distribution of the dry season is estimated in February-April, June-September and the rainy season in October-January and May.

Loglinear Model Formation using Hierachial Backward Method

2020-03-02T12:37:53+07:00

The loglinear model is a special case of a general linear model for poisson
distributed data. The loglinear model is also a number of models in statistics that are used to
determine dependencies between several variables on a categorical scale. The number of
variables discussed in this study were three variables. After the variables are investigated,
the formation of the loglinear model becomes important because not all the model
interaction factors that exist in the complete model become significant in the resulting
model. The formation of the loglinear model in this study uses the Backward Hierarchical
method. This research makes loglinear modeling to get the model using the Hierarchical
Backward method to choose a good method in making models with existing examples.
From the challenging examples that have been done, it is known that the Hierarchical
Reverse method can model the third iteration or scroll. Then, also use better assessment
methods about faster workmanship and computer-sponsored assessments that are used more
efficiently through compatibility testing for each model made