Mathematical Philosophy

Mathematics and philosophy are two words with different meanings and the same thing. With various historical evidence, mathematics as the basis of science is not part of or born from philosophy. In the same position in knowledge, mathematics confirm the answers to intimate problem in philosophy. Often there is confusion in philosophy because of conflicting concepts with one another. Mathematics without philosophy does not move swiftly, because without the meanings that are sometimes driven by philosophy. Logically, truth is not well developed in evidence except when mathematics and philosophy get long. It is to provide an understanding of the need for a foundation of truth thought, which generally reveals in the comprehension of mathematics, namely in meta-mathematics and philosophy.

The Cesàro Operator on Some Sequence Spaces in Riesz Space of Non-Absolute Type

The Cesàro operators are investigated on the class -valued sequence spaces , and with is a Riesz space. Besides, we also carry out that are order bounded operators.

Another Proof of An Uncountability Real Numbers Set

The diagonalization argument is one way that researchers use to prove the set of real numbers is uncountable. In the present paper, we prove the same thing by using the supremum property in the set of real numbers.

Some Vector FK Sequence Spaces Generated by Modulus Function

In this paper, some vector valued sequence spaces and using modulus function are presented. Furthermore, we examined some topological properties of these sequence spaces equipped with a paranorm.

Vertex Exponent of Asymmetric Two-coloured Cycle

This paper is about an asymmetric two-coloured cycle. Let D be an asymmetric two-coloured cycle on n vertices, where n is odd and n >= 3, we show that the exponent of the k-th vertex of D is exactly (n2-1)/4 + ⌊ k/2 ⌋.

Loglinear Model Formation using Hierachial Backward Method

The loglinear model is a special case of a general linear model for poissondistributed data. The loglinear model is also a number of models in statistics that are used todetermine dependencies between several variables on a categorical scale. The number ofvariables discussed in this study were three variables. After the variables are investigated,the formation of the loglinear model becomes important because not all the modelinteraction factors that exist in the complete model become significant in the resultingmodel. The formation of the loglinear model in this study uses the Backward Hierarchicalmethod. This research makes loglinear modeling to get the model using the HierarchicalBackward 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 HierarchicalReverse method can model the third iteration or scroll. Then, also use better assessmentmethods about faster workmanship and computer-sponsored assessments that are used moreefficiently through compatibility testing for each model made

Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach

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 varianceσ 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 + 12 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 ∫ 2π [f ′′ (t)] 2 dt π0 } . 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

Existence of Polynomial Combinatorics Graph Solution

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.

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

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probabilitydistribution 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.

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

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.