cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Hidayat
Contact Email
jmea@umsu.ac.id
Phone
+6285361162933
Journal Mail Official
jmea@umsu.ac.id
Editorial Address
Magister Pendidikan Matematika Program Pascasarjana Universitas Muhammadiyah Sumatera Utara, Jl. Denai No 217, Medan, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Mathematics Education and Application (JMEA)
Published by Universitas Muhammadiyah Sumatera Utara
ISSN : -     EISSN : 28291514     DOI : DOI: http://dx.doi.org/10.30596%2Fjmea.v1i2
Core Subject : Education,
Education Mathematics
Journal of Mathematics Education and Application (JMEA) menerima artikel dan mempublikasikan hasil kajian/penelitian ilmiah tentang Pendidikan dan Aplikasi Matematika dan yang berkaitan. Penyebarluasan penelitian bertujuan untuk membangun peradaban bangsa serta mengembangkan Ilmu Pendidikan dan Aplikasi Matematikadan teknologi dalam meningkatkan sumber daya manusia. Focus & Scope 1. Learning Media for Mathematics Education 2. Mathematics Education Curriculum Development 3. Development of Mathematics Education Teaching Materials 4. Research and Teaching Mathematics Education 5. Application of Mathematics Applications 6. Applications of Mathematics Science
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 2 (2023): Juni" : 5 Documents clear
Estimation of Multivariate Adaptive Regression Splines (MARS) Model Parameters by Using Generalized Least Square (GLS) Method Nurul Azizah Rahmadani Ritonga
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13106

Abstract

The regression analysis method for estimating the regression curve is divided into 3 (three) categories, namely parametric regression analysis, non-parametric regression analysis, and semi-parametric regression analysis. One form of non-parametric regression model is spline which can be developed into Multivariate Adaptive Regression Splines (MARS). The OLS estimation method will get good estimation results compared to other methods if the classical assumptions are fully met. However, if the classical assumptions cannot be fulfilled, this method is not good enough to use. The GLS method can be used if the classical assumptions required by the OLS method are not met. This study aims to estimate the parameters of the MARS model using the GLS method. The GLS method can be used if the classical assumptions required by the OLS method are not met. An example of a case used in the application of non-parametric estimation of the MARS model is the data on the number of doctors and gross enrollment rates for tertiary institutions in 32 districts/cities in North Sumatra in 2021. The best MARS model obtained in this study was obtained with a knot point of 21.2, 24 .2 and 27.2, with BF=6, MO=3, MI=0 with a GCV value of 6628.965. The best model obtained based on this research is as follows: 
The Application of Fuzzy Logic in Optimization Pulp in Pt.Toba Pulp Lestari, Tbk With the Mamdani Method Dony Pakpahan; Putri Khairiah Nasution
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13335

Abstract

Fuzzy logic is used to show data or information that is certain. This survey examines the used of fuzzy logic in optimizing production pulp at PT. Toba Pulp Lestari, Tbk using the Fuzzy-Mamdani approach. Constraints faced include the uncertain amount of pulp production from time to time . The steps in solving these problems, namely: (1) is to form a fuzzy set and determine the conversation. Next, (2) is to find out the fuzzyfication that changes the input into fuzzy. Next, (3) is the formation of fuzzy rules with the max method. (4) is defuzzification with MOM method. The problem solving is assisted with the assistance of the Matlab software application. The data in this study are the quantity of production, the quantity of stock and the number of requests from January 2021-December 2021. Based on the data obtained using the Mamdani method, it is known that the optimal production based on the amount of demand and supply is January 13,300 ton, February 18,200 ton, March 8,110 ton, April 10,700 ton, May 10,600 ton, June 13,400 ton, July 12,000 ton, August 10,700 ton, September 18,800 ton, October 18,300 ton, November 10,100 ton, December 10,400 ton.
Student Satisfaction Analysis of Service Quality University of Sumatera Utara (USU) Library with Fuzzy Service Quality Method Mirdayani Zega; James Peter Marbun
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13592

Abstract

Lack of service quality becomes one obstacle to student satisfaction. Service quality can be known by comparing the service that is in real acceptable with the service that the student expects. In this research method used to measure the level of satisfaction of acceptable service and which the student expects is the Fuzzy Service Quality method. The Fuzzy method is a method used to resolve issues where descriptions of activities, research and assessment are subjective, uncertain and inappropriate. The Fuzzy method is combined with the Service Quality method so that the student’s perception and expectation measurement can be measured easily and precisely. In Service Quality There are five dimensions that are used to improve the quality of service such as Tangible, Reliability, Responsiveness, Assurance and Empathy. The results of this study show that the gap value of the five dimensions has a negative value, meaning that the quality of service has not been expected so that the students perceived dissatisfaction occurs. This indicates that the quality of service provided by the library needs to make repairs, one of them on the physical attributes (Tangible) 6 with the availability question and ease of Internet access. At the value of gap per dimension, the dimensions that need to be prioritized by the library of USU to be done improvement is the dimension of Empathy.
Analysis of the Influence of E-learning Services on User Satisfaction with Structural Equation Modeling (Case Study: Mathematics Student at University of North Sumatra) Putri Patresia Sihombing
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.15285

Abstract

E-learning has been implemented at the University of North Sumatra as a means of supporting PBM (teaching and learning process) since the beginning of Covid-19. Courses that already use the PMB are one of the objects in this study. The factors that will be analyzed include content, accuracy, form, timeliness, security and privacy, and the speed of media response to user satisfaction. In this thesis, the analysis of the effect of e-learning services on student satisfaction will be analyzed using a structural equation model (SEM) approach. . SEM is a multivariate analysis used to analyze the relationship between variables. SEM is used to assess and justify a model according to Hair et.al (2006). The main requirement for using SEM is to build a hypothetical model consisting of a structural model and a measurement model in the form of a path diagram. The results of the analysis in the study show that not all factors influence the level of satisfaction with e-learning services at the University of North Sumatra. Of the six factors, such as content (X1), accuracy (X2), form (X3), timeliness (X4), security and privacy, and media response speed (X6), only the media response speed factor (X6) shows a significant effect. on user satisfaction (Y1). So based on the data analysis techniques that have been carried out in this study, there is only one factor that influences e-learning user satisfaction, namely accuracy. With a CR value of 1.916 where the value is greater than the critical value of 1.65 with a coefficient of 0.320
Zero-Inflated Poisson Regression Testing In Handling Overdispersion On Poisson Regression Mutia Sari; Open Darnius
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13591

Abstract

The classical linear regression analysis is an analysis aimed at knowing the relationship between the response variables and the explanatory variables assuming the normal distribution data, but in the applied data is often not the case. Generalized Linear Model (GLM) was developed for data in the form of categorical and discrete distribution. In this study the data was raised which has a poisson distribution by as much as n, with average  λ and the odds appearing zero p. Poisson regression is GLM for Poisson-distributed data assuming that Var(X ) = E(X ), but asusumption is rare in applied data. For rare occurrences of a specified interval X variables are often zero-valued, thus causing overdispersion (Var(X ) E(X )). Lambert (1992) introduced a method for overcoming overdispersion in poisson regression i.e. the Zero-Inflated Poisson regression (ZIP). In this research conducted a ZIP regression test in overcoming overdispersion to see the opportunity limit p appears zero- valued as the value that causes overdispersion. Testing is done with RStudio ver. 1.1.463.0 software. Based on the simulated data obtained that Regression ZIP stopped overcoming overdis persion at the condition n = 500, λ = 0.7 with the odds p = 0.2 with a dispersion ratio of  τ = 1.010.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 4, No 3 (2025): Oktober Vol 4, No 2 (2025): Juni Vol 4, No 1 (2025): Februari Vol 3, No 3 (2024): Oktober Vol 3, No 2 (2024): Juni Vol 3, No 1 (2024): Februari Vol 2, No 3 (2023): Oktober Vol 2, No 2 (2023): Juni Vol 2, No 1 (2023): Februari Vol 1, No 3 (2022): Oktober Vol 1, No 2 (2022): Juni Vol 1, No 1 (2022): Februari Vol 1, No 1 (2020) Vol 1, No 1 (2012): EUREKA More Issue