Modeling Equations Research Articles

The Influence Of Communication, Leadership Style And Organizational Culture On Job Satisfaction With Work Motivation As Intervening Variables At PT Hki Binjai Project Brandan Zone 2

The purpose of this study is to see the influence of communication, leadership style and organizational culture on job satisfaction with work motivation as an intervening variable in PT Hutama Karya Infrastruktur Project Binjai Brandan Zone 2. This research uses a quantitative approach, with statistical methods of path analysis. The population in this study is all contract and permanent employees of PT Hutama Karya Infrastruktur Project Binjai Brandan Zone 2 totaling 86 people. The sampling technique uses saturated samples so that the number of samples is 86 respondents. Partial Least Square (PLS) data analysis technique with Structural Equation Modeling (SEM) equation model. The results showed organizational culture, leadership style, communication, and motivation had no effect on job satisfaction. Leadership style variables have no effect on motivation, while organizational culture and communication affect work motivation. The results of the indirect influence test show that leadership style and organizational culture affect job satisfaction through work motivation as an intervening variable, and communication has no effect on job satisfaction through work motivation as an intervening variable.

Empirical Approach on Uncertainty Analysis of Differential Equations in Mathematical Modeling

Uncertainty theory, introduced by Professor Baoding Liu of Tsinghua University in China in 2007, has lately made significant progress. It serves as an advanced and adaptable mathematical tool for modeling uncertainties and handling unforeseen outcomes that may arise when using likelihood and/or fuzzy set approaches, which are sometimes favored alternatively. Mathematical models are used in several academic disciplines to provide accurate quantitative estimations based on facts. Crucially, these mathematical frameworks must offer a dependable evaluation of the certainty in their predictions. Uncertainty quantification (UQ) is a discipline that focuses on delivering accurate and dependable assessments of trust regarding forecasts made by models. Empirical models utilize data and statistical methods to build connections between parameters in a system, whereas mechanistic frameworks are built upon prior knowledge and understanding of the underlying mechanism that governs changes in the overall structure. This article will demonstrate the effectiveness of a data-driven empirical technique in solving ordinary differential equations under conditions of uncertainty. The research aims to determine the practicality and accuracy of its approach compared to existing numerical methods, which may often yield unsatisfactory results.

Implementation of the Hybrid ADI-FDTD Scheme to Maxwell Equation for Mathematical Modeling of Breast Tumor

Breast cancer is the most common cancer in women, and non-destructive detection of the tumor is vital. The interaction of electromagnetic waves with breast tissue and the behavior of waves after interaction are used to model tumor detection mathematically. The behavior of electromagnetic waves in a medium is described using Maxwell's equations. Electromagnetic waves propagate according to the electrical properties of a medium. Since the electrical properties of tumor tissue are different from those of normal breast tissue, it is assumed that the tumor is a lossy dielectric sphere, and the breast is a lossy dielectric medium. Under this assumption, Maxwell's equations are used to calculate the scattered field from the tumor. The field scattered by the tumor is different from other tissues because their dielectric properties are different. The location and size of the tumor can be determined by utilizing the difference in scattering from the tissues. While the scattering field from the tumor in spherical geometric form is analytically calculated, it is not analytically possible to calculate the scattering field from the tumor in different geometric shapes. In addition to non-destructive detection of the tumor, an efficient numerical method, the finite difference time domain method (FDTD), is used to simulate the field distribution. After the location of the tumor is determined, the Alternating Direction Implicit (ADI) FDTD method, which gives simulation results by dividing the computation domain into smaller sub-intervals, can be used. Scattered fields are calculated analytically in the geometry where the tumor is in the form of a smooth sphere, and in more complex geometry, the field distributions are successfully obtained with the help of MATLAB using FDTD and ADI-FDTD algorithms.

The effect of mixing on the dispersibility of adhesive mixtures for inhalation. Comparison of high shear and Turbula mixers

This study investigates the effect of different mixers and the applicability of the mixing energy (ME) concept to dry powder formulations for inhalation. With the aim to step-wise build and expand this concept, adhesive mixtures of 2 % budesonide and lactose carrier were investigated, both with 1 % magnesium stearate (MgSt) added in a ‘coating’ step, and without, the latter referred to as ‘naked’ formulations. For high shear mixed formulations, the fine particle fraction (FPF) was found to increase with increasing ME up to 60 % and thereafter decreased, using the Novolizer device. The data could be well fitted to the modeling equation, thus confirming the validity of the ME concept. The naked formulations displayed a linear decrease in FPF with increasing ME, again showing the validity of the ME concept.For Turbula mixed formulations, FPF increased with increased mixing time (and mixing energy) for all batches. The naked (binary) composition reached to higher FPF values than for high shear mixing and the formulation with MgSt reached to FPF values around 60 %, demonstrating that it is possible to achieve the same high drug dispersibility with the Turbula mixer as for high shear mixer.An equation for calculation of mixing energy in Turbula mixing was set up in an analogous way to the equation for high shear mixing, which enabled direct comparison between the two mixers.

How do family support factors influence college students’ online-startup thinking?

PurposeThe purpose of this study is to present the relationship between family support factors and Chinese college students’ online-startup thinking on live streaming platforms. Considering China's specific online entrepreneurial environment, this paper divides Chinese college students’ online-startup thinking according to the liberal–conservative thinking theory. This study classifies family support factors based on the tangible–intangible resource division theory. Different tangible and intangible factors have different impacts on their online-startup thinking.Design/methodology/approachThis study tests 588 samples based on the partial least squares path modelling and variance-based structural equation modelling. This study promotes importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for Chinese college students and related departments.FindingsTangible family support factors, such as labour resources support, and intangible family support factors, such as verbal encouragement, can positively enhance Chinese college students’ liberal thinking to online-startup and decrease their conservative thinking. Meanwhile, according to importance-performance map analysis results, verbal encouragement from the intangible unit instead of financial resource support from the tangible unit has a higher total effect and performance on Chinese college students’ liberal thinking and conservative thinking.Originality/valueThis study draws on psychology research based on Chinese college students’ unique entrepreneurial mentality. This paper divides Chinese college students’ thinking in online-startups into liberal thinking and conservative thinking based on the liberal–conservative thinking theory. Meanwhile, according to the feature of Chinese family support factors, this paper classifies various elements based on the tangible–intangible resource division theory, which is helpful for scholars to understand that the student perceptions of the value of family support are critical to the success of the online-startup.

Evaluation of constitutive equations for modeling and characterization of microstructure during hot deformation of sintered Al–Zn–Mg alloy

Commercial simulation software like QFORM and DEFORM do not possess adequate material data for alloys produced through powder metallurgy. Therefore, in the present study, hot compression tests of Al-5.6%Zn–2%Mg aluminum alloy were conducted at temperature ranges (300 0C-500 °C) and strain rates (0.1 s−1-0.0001 s−1). The impact of compression temperature and strain rate on the flow curve behaviour and microstructure evolution-associated mechanisms were investigated through experimentally acquired flow curves and EBSD analysis. Four constitutive models were constructed; namely the Arrhenius-type, modified Johnson Cook (MJC), modified Zerilli-Armstrong (MZA), and an artificial neural network (ANN). The results demonstrated that among the models considered, the ANN and Arrhenius-type models exhibited the lowest AARE values of 0.486 % and 3.36 %, respectively. Conversely, the MZA and MJC models displayed higher AARE values of 8.84 % and 3.93 %, respectively. Notably, the Arrhenius-type model emerged as the most suitable prediction model due to its capability to handle nonlinear relationships between factors. However, in scenarios where material properties are unknown or experimental data is limited, the MJC model can serve as a simpler alternative. The MZA model was deemed unsuitable for accurately estimating flow stress in hot compression. Remarkably, the best-trained ANN model exhibited the highest predictive performance with an AARE of 0.486 % and an R-value of 0.99. This study offers fundamental insights to improve the accuracy of simulating hot compression procedures. EBSD analysis demonstrated that higher upsetting temperatures and lower strain rates promoted recrystallization, resulting in a more uniform microstructure.

The Laser Selective Sintering Controlled Forming of Flexible TPMS Structures.

Sports equipment crafted from flexible mechanical metamaterials offers advantages due to its lightweight, comfort, and energy absorption, enhancing athletes' well-being and optimizing their competitive performance. The utilization of metamaterials in sports gear like insoles, protective equipment, and helmets has garnered increasing attention. In comparison to traditional truss and honeycomb metamaterials, the triply periodic minimal surface lattice structure stands out due to its parametric design capabilities, enabling controllable performance. Furthermore, the use of flexible materials empowers this structure to endure significant deformation while boasting a higher energy absorption capacity. Consequently, this study first introduces a parametric method based on the modeling equation of the triply periodic minimal surface structure and homogenization theory simulation. Experimental results demonstrate the efficacy of this method in designing triply periodic minimal surface lattice structures with a controllable and adjustable elastic modulus. Subsequently, the uniform flexible triply periodic minimal surface lattice structure is fabricated using laser selective sintering thermoplastic polyurethane technology. Compression tests and finite element simulations analyze the hyperelastic response characteristics, including the element type, deformation behavior, elastic modulus, and energy absorption performance, elucidating the stress-strain curve of the flexible lattice structure. Upon analyzing the compressive mechanical properties of the uniform flexible triply periodic minimal surface structure, it is evident that the structure's geometric shape and volume fraction predominantly influence its mechanical properties. Consequently, we delve into the advantages of gradient and hybrid lattice structure designs concerning their elasticity, energy absorption, and shock absorption.

DESIGN And IMPLEMENTATION Of HYDRIC CONTROLLER For TWOWHEELED ROBOT

The two-wheeled robotic machine (TWRM) with five degrees of freedom (DOF) is a type of mobile robot that consists of two wheels and a body that can rotate and move in different directions. The proposed architecture of the TWRM in this paper features five DOFs, which allows for greater flexibility and range of motion. However, this also makes controlling the system more challenging, as thecenter of mass (COM) changes while performing tasks in multiple directions. To address this issue, the study utilizes a state-space model created by linearizing the non-linear modeling equations at the equilibrium point using the Lagrangian modeling. To stabilize the TWRM, several controllers were evaluated, including PID, LQR, and LQR with PID methods. The PID controller is a popular feedbackcontrol strategy that adjusts the control output based on the error between the desired and actual values of the systemoutput. LQR is a state-space control technique that aims to minimize a cost function that expresses the system's performance criteria. The PID with LQR technique combines the strengths of both strategies, with PID used as a feed-forward to control the intermediate body (IB) and end-effector and LQR used as state feedback to regulate all states. The controllers were evaluated under variouscircumstances, including disturbance signals, trackingpathways, and moving actuators. Simulation results showedthat the LQR with LQR controller performed better thanthe other two control systems in terms of least overshoot,rising time, and applied input forces. This suggests that thePID with LQR technique is a robust and effective controlstrategy for stabilizing the TWRM and improving itsperformance for various tasks.

Digital Module 34: Introduction to Multilevel Measurement Modeling

Module AbstractClustered data structures are common in many areas of educational and psychological research (e.g., students clustered in schools, patients clustered by clinician). In the course of conducting research, questions are often administered to obtain scores reflecting latent constructs. Multilevel measurement models (MLMMs) allow for modeling measurement (the relationship of test items to constructs) and the relationships between variables in a clustered data structure. Modeling the two concurrently is important for accurately representing the relationships between items and constructs, and between constructs and other constructs/variables. The barrier to entry with MLMMs can be high, with many equations and less‐documented software functionality. This module reviews two different frameworks for multilevel measurement modeling: (1) multilevel modeling and (2) structural equation modeling. We demonstrate the entire process in R with working code and available data, from preparing the dataset, through writing and running code, to interpreting and comparing output for the two approaches.

Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI

Objective. New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC ( ML.FC ) which efficiently captures linear and nonlinear aspects. Approach. To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity ( ML.EC ), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality ( SP.GC ) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms of reproducibility and the ability to predict individual traits in order to demonstrate these measures’ internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits. Main results. The proposed new FC measure of ML.FC attains high reproducibility (mean intra-subject R 2 of 0.44), while the proposed EC measure of SP.GC attains the highest predictive power (mean R 2 across prediction tasks of 0.66). Significance. The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Universal differential equations for glacier ice flow modelling

Abstract. Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal differential equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i.e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a two-dimensional shallow-ice approximation partial differential equation with an embedded neural network, i.e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions. We implemented this modelling framework as ODINN.jl, a package in the Julia programming language, providing high performance, source-to-source automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼ 500 000 ordinary differential equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability in ice creep and basal sliding for ice flow, and new hybrid surface mass balance models.

Detailed and simplified plasma models in combined-cycle magnetohydrodynamic power systems

Magnetohydrodynamics (MHD) is a subject concerned with the dynamics of electrically conducting fluids (plasma) and can be applied in electric power generation. As a unique technology for producing direct-current electricity without moving parts, it can be utilized within a high-temperature topping power cycle to be combined with a traditional bottoming power cycle, forming a combined-cycle MHD system. This study presents governing equations for the electric field and current density field within a moving plasma subject to an applied magnetic field. The modeling equations are described at four descending levels of complexity. Starting with the first level of modeling, which is the most general case, where no assumptions are made regarding the electric field, plasma velocity field, applied magnetic field, or electrode geometry. In the second level of modeling, the magnetic field is treated as one-dimensional. In the third level of modeling, a specific Faraday-type magnetohydrodynamics plasma generator channel is considered, having two continuous electrodes acting as parallel constant-voltage terminals. In the fourth (and simplest) level of modeling, an additional approximation is made by setting the Hall parameter to zero and replacing all vector fields with scalar quantities. For that simplest model, a representative set of operation conditions (electric conductivity 20 S/m, temperature 2800 K, supersonic plasma gas speed 2000 m/s with Mach 2.134, and magnetic flux density 5 T) shows that the optimum idealized electric power that can be extracted from a unit volume of plasma is estimated as 500 MW/m3. This is a much larger volumetric power density than typical values encountered in reciprocating piston-type engines (0.2 MW/m3) or rotary gas turbine engines (0.5 MW/m3). Such an extremely high power density enables very compact power generation units.

Pemodelan Pemilihan Moda Transportasi (Studi Kasus: Fakultas Ekonomi dan Bisnis Universitas Negeri Malang)

A traveler will consider many things in choosing a mode because the availability of transportation modes is variated. The traveler's characteristics, the trip's characteristics, and the facilities of transportation mode characteristics can influence a person's decision in choosing a mode. The study of the selection of modes of transportation to campus by students, lecturers, and other workers at the Faculty of Economics and Business, State University of Malang, aims to describe the characteristics of mode users and determine the mode selection models to go to campus. This study used a descriptive analysis method to determine the characteristics of mode users and the binomial difference logit method for modeling transportation. The results of the study showed that five factors are most influential in choosing a mode between private transportation and public transportation at the Faculty of Economics and Business, the State University of Malang, namely ownership of a driving license, travel distance, the comfort of transportation, the safety of transportation, and availability of transportation which produces a regression model UKP – KU = -11.480 + 4.045 (X7) – 1.263 (X8) + 5.102 (X11) + 2.302 (X12) + 5.693 (X14). This model has an R2 value of 0.743 and a classification plot of 0.927. From the results of the modeling equation, the probability value of mode selection for private transportation is 0.997. In contrast, public transportation gets a probability value of 0.003.

The Impact of Product Quality and Brand Image on Repurchase Intention Through Customer Satisfaction

The importance of repurchasing intention indicates a product's success or failure in the market. This study aimed to determine and analyze the effect of product quality and brand image on repurchase intention through consumer satisfaction with Scarlett Whitening products. The data processing method in this study used the Structural Equation Modeling (SEM) modeling equation with a total of 135 respondents. This study uses a quantitative analysis approach that adopts Partial Least Square (PLS). This study uses inferential statistical data analysis. The first finding is that product quality positively affects customer satisfaction. The brand image on customer satisfaction is declared influential or accepted. Product quality has a positive effect on repurchase intention and is accepted. The effect of brand image on repurchase intention is declared to have no effect, or the hypothesis is proven to be rejected. Customer satisfaction has a positive effect on repurchase intention. The results empirically prove if product quality affects repurchase intention, mediated by consumer satisfaction. Brand image has a positive effect on repurchase intention mediated by consumer satisfaction. This study has implications for management that a good brand image of Scarlett body lotion will generate fame or can be said to attract consumers' attention, and quality Scarlett body lotion products will also increase consumer repurchases.

INVESTIGATION OF THE EFFECTIVENESS FOR PROTECTIVE SCREENS OF VARIOUS FORMS ON AIR POLLUTION REDUCTION

Problem statement. The task of evaluating the effectiveness of the use of protective screens of different geometric shapes to reduce the level of air pollution is considered. The data on the screen allow you to change the aerodynamics of the air flow and redirect the movement of polluted air away from the work areas in the other direction. The purpose of the article – study of the effectiveness of the use of protective screens of different geometric shapes to reduce the level of pollution, creation of a three-dimensional numerical model for the analysis of the effectiveness of the use of protective screens. Methodology. The method of physical experiment in laboratory conditions is used to analyze the effectiveness of screens of different geometric shapes. Three-dimensional equations of aerodynamics and mass transfer are used for mathematical modeling of impurity propagation in the presence of screens. The developed numerical model makes it possible to take into account the air flow velocity profile, atmospheric diffusion, the emission intensity of the impurity, the rate of gravitational sedimentation of the impurity in the air. For the numerical integration of the modeling equations of aerodynamics and mass transfer, finite-difference splitting schemes are used. Scientific novelty. Data on the effectiveness of the use of four protective screens, which differ in geometric shape, were obtained experimentally. Experimental data make it possible to carry out an initial assessment of the impact of various screens on reducing the level of pollution in working areas. A fast-calculating 3D numerical model was developed for solving problems of aerodynamics and mass transfer in relation to the problem of evaluating the effectiveness of the use of screens located in areas where atmospheric air pollution occurs. Practical significance. Experimental data make it possible to justify the choice of a protective screen near the highway or another area on the industrial site where the impurity emission takes place. A computer code was created on the basis of the developed mathematical model, which makes it possible to predict the intensity of air pollution in the presence of obstacles that change the aerodynamics and the direction of transport of impurities in the atmosphere. Conclusions. The results of the physical experiment allow us to imagine the regularities of the formation of areas of pollution near protective screens of different geometric shapes, the developed mathematical model allows us to estimate air pollution in areas where there are obstacles in the way of the movement of impurities. The results of physical and computational experiments are presented.

Traffic Impact Analysis of Tajur Bogor Mall Boxies Building on Roads

Traffic congestion that occurs almost evenly throughout the Bogor City area can be caused by several main factors, namely. The narrower roads and the increasing number of motorized vehicles result in congestion. The area of Bogor City, which is only 11,850 ha with a road length of 783,412 km, is already congested to accommodate the number of vehicles that are increasingly exceeding the carrying capacity of the road. In addition, the rapid development of business and trade areas in the city of Bogor spurred changes in traffic intensity. This study aims to create a model of generation and attraction caused by land use such as Educational Buildings, Industrial Estates, Traditional Markets and Supermarkets located in the sub-district. Research data retrieval is carried out using primary and secondary data methods, then the data is processed using (Trip Generation Manual) then modeled with (Software Saturn). The number of generation and towing in the studied area is 491 trips/hour. With Service Levels ranging from C to F. Then the design equation for transportation modeling for the East Bogor sub-district in 2021 is Y = 2963.66+0.840(X) then in 2026, namely Y = 2963.66+0.840(X).

The Potential of Budget Participation and Dalihan Natolu Leadership Style to Improve Community Welfare in the Toba Caldera Tourism Geography Area

Objective: This research analyzes the determinants of budgetary participation's effect on financial performance through Dalihan Natolu's leadership style in 4 districts in the Toba Caldera region and conducts a descriptive economic analysis of social and economic indicators in 7 districts post-Regional Autonomy and Fiscal Decentralization. Analyze village category development using the village development index (IDM) in 7 Toba Caldera districts. Estimating economic development parameters in 7 Toba Caldera districts in North Sumatra. Method: This study uses a quantitative method with descriptive analysis. Descriptive statistical analysis in this study was carried out using frequency distribution analysis and inferential statistical analysis including: evaluation of the assumptions of the structure equation model (SEM), SEM analysis, model fit test and hypothesis testing. The data analysis technique in this study uses Partial Least Square (PLS). PLS is a Structural Equation Modeling (SEM) equation model with an approach based on variance or component-based structural equation modeling. This research was conducted in 4 (four) administrative districts located around the Lake Toba Tourism Area which in their kinship life absolutely adhere to the local wisdom of Dalihan Natolu, namely Toba Regency, Samosir Regency, North Tapanuli Regency, Humbang Hasundutan Regency. Conclusion : This quantitative study's findings Budget Participation Factor also affects Financial Performance moderated by Dahlian Natolu's Leadership Style, as shown by t statistics > t table (3,961 > 2,032). To strengthen this analysis, you can also use the P Value, with the condition p < 0.05, which shows 0.000 < 0.05. In 2019, the Human Development Index in 7 Toba Caldera Region Regencies measured Life Expectancy (UHH), Average Length of Schooling (RLS), and Expenditure Per Capita. In 2019–2020, the Toba Caldera Region's Gross Regional Domestic Product (GRDP) is stable and led by Agriculture, Forestry & Fisheries, Wholesale and Retail Trade, and Industrial Management. Karo Regency has the highest HDI and Humbang Ha Anglean the lowest. The comparative IPD for that year increased in 7 Toba Caldera Region Regencies from 2014 to 2018. Toba Caldera districts' Human Development Index (IPM) increases somewhat with GRDP per capita.

Phase Changing Materials

Building envelope is a key element in providing adequate energy and thermal comfort performance to buildings. The main techniques adopted in this context are discussed to identify modern and effective methods with a particular focus on phase change materials (PCMs). Incorporating PCMs with building construction materials is a booming technology, owing to their enhancement potential of storing and releasing heat during phase transition. This work highlights the importance of PCMs in building envelope, focusing on roof and external wall applications. PCM types, general and desired properties and application area are presented and discussed. Incorporation techniques and methods, main numerical tools, and modelling equations are used to describe the thermal behaviour of PCM. A comprehensive assessment on the basis of recent studies has been conducted to point out the potential of PCM with the most appropriate techniques under different locations. The main findings of PCM thermal performance have been described, considering the cooling/heating load reduction, energy-saving and thermal comfort gained. Keywords: PCMs, PCM-integrated buildings, Building envelope, Thermal comfort, Energy saving, Heating/cooling load reduction.

A coefficient identification problem for a system of advection-reaction equations in water quality modeling

A coefficient identification problem for a system of advection-reaction equations in water quality modeling

DATA ON THE VALUE OF ISLAMIC RELIGIOUS KNOWLEDGE AND SYSTEM OF MATHEMATICAL MODELING EQUATIONS INCREASING THE VALUE OF ISLAMIC RELIGIOUS KNOWLEDGE

The understanding of Islamic Religion of students in the Engineering Mathematics Course, Department of Mathematics, Universitas Islam Negeri Sunan Kalijaga Yogyakarta is quite good, with an average value = 86.4444, median = 84, and mode = 100. The mathematical modeling equation system for increasing understanding of Islamic religion consists of four ordinary differential equations.