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Muhammad Hidayat
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Magister Pendidikan Matematika Program Pascasarjana Universitas Muhammadiyah Sumatera Utara, Jl. Denai No 217, Medan, Indonesia
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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 66 Documents
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Binary Logistic Regression Analysis Using Stepwise Method on Tuberculosis Events Rifan halomoan tua sinaga; open darnius
JMEA : Journal of Mathematics Education and Application Vol 2, No 1 (2023): Februari
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i1.12164

Abstract

Tuberculosis is an infectious disease caused by the bacteria Mycobacterium tuberculosis. Among all the districts/cities of North Sumatra province, Medan has the highest cases of tuberculosis sufferers with a total of 12,105 cases in 2019. This study aims to determine the factors that significantly influence tuberculosis. The factors analyzed were age, gender, occupation, education, BCG immunization, history of diabetes mellitus and HIV infection. This study uses secondary data for the period January 2019 to December 2020 obtained from the Sentosa Baru Health Center. With the help of SPSS, this study uses a stepwise method with forward selection and backward elimination as the method for analysis. Akaike Information Criterion (AIC) is used to select the best model in the stepwise method. With the AIC criteria obtained, the best model is forward selection because the AIC value is lower at 28,527 compared to backward elimination at 41,664. Of the 7 variables studied, there are 3 factors that have a significant effect, namely age, history of diabetes mellitus, and HIV infection so that the model g(x) = 2.802 1.056 X1 0.614 X6 2.477 X7.
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.
The Application Of Servqual Method and Importance Performance Analysis (IPA) in Analyzing The level of Patient Satisfaction With the Quality of Service at Wira Husada Kisaran General Hospital Sirait, Naomi Natasya; Marbun, James Piter
JMEA : Journal of Mathematics Education and Application Vol 2, No 3 (2023): Oktober
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i3.17096

Abstract

One of the health care facilities are hospital. Hospitals are required to provide quality services to give satisfaction to the patient. Services that are less than optimal will reduce the number of patients visiting. Therefore, improving the quality of service is needed in an effort to attract patients to seek treatment at the WiraHusadaKisaran General Hospital. This research was conducted by distributing 70 questionnaires and calculations were carried out using the Importance Performance Analysis (IPA) method and the Servqual method. The Servqual method is used to determine the level of patient satisfaction and the Importance Performance Analysis (IPA) method is used to determine which attributes need improvement. From the results of the calculation of the Servqual method using 5 dimensions of service, namely tangible, reliability, responsiveness, assurance, and empathy to determine the value of GAP. On the tangible dimensions of the attributes that have not satisfied the patient, the comfort and cleanliness of the hospital and inpatient environment. In the dimension of reliability attributes that have not satisfied the patient, the doctor conducts an examination of the patient according to the specified schedule and on time. In the dimension of responsiveness attributes that have not satisfied patients, namely, nurses quickly and responsively serve patients. On the assurance dimension has satisfied patients. In the dimension of emphaty attributes that have not satisfied the patient, namely, medical personnel respond to patient complaints. From the results of the calculation of the Importance Performance Analysis (IPA) method, it has a suitability level value of 79.34% and in the Cartesian diagram of the 20 attributes there are 6 attributes that require repair and improvement, namely attribute numbers 1, 3, 4, 8, 9, and 12.
Comparison of Newton Raphson Method and Ridge Method In Probit Regression Parameter Estimation Yastri, Yastri; Pane, Rahmawati
JMEA : Journal of Mathematics Education and Application Vol 2, No 3 (2023): Oktober
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i3.13327

Abstract

Probit regression model is a non-linear model used in the process of analyzing the relationship between a response variable that has categorical properties. The problem that is very often experienced in probit regression when the predictor variable consists of one or more is that there is a very high correlation between predictor variables called multicollinearity. To overcome this, the Newton Raphson method and the Rigde method are used. So this research was conducted to compare the Newton Raphson method and the Ridge method in the estimation of the Probit Regression parameter. The data used in this research is 1000 data generation that contains multicollinearity. Based on this research, the estimated mean square error of the Probit Regression model using the Newton Raphson method is 0.488. The estimation result of the mean square error of the Probit Regression model using the Ridge method is 0.488. The results of this study indicate that the estimation of the Probit Regression parameter using the Newton Raphson method is as good as the Ridge method. This can be seen from the estimated value of MSE using the Newton Raphson method and the Ridge method. This can happen due to the small value of the langrage multiplier obtained, so it does not have an impact on the model obtained.
Application of Goal Programming Method in Production Optimization of Crude Palm Oil and Crude Palm Karnel Oil (Case Study: Pt. Barumunagro Sentosa) Rizwana, Annisa; Nasution, Putri Khairiah
JMEA : Journal of Mathematics Education and Application Vol 2, No 3 (2023): Oktober
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i3.13340

Abstract

Planning in production is an action from a company that can determine the success of a company. PT. BarumunAgro Sentosa is a company that owns palm oil plantations and mills. This study aims to analyze the methodGoal Programming and its completion in production planningCrude Palm OilandCrude Palm Kernel Oil in the period January – December 2022 Completion of the methodGoal Programming in optimizing the production of CPO and CPKO in this study using the help of LINDO software (Linear Interactive Discrete Optimizer). Completion of this method first performs a projection or forecast of the number of requests obtained from the previous period's demand data with Minitab software. The results obtained from this study are that the optimal amount of CPO production for the period January - December 2022 is 74,803,459 kg from the initial target of 73,420,955 kg and for the optimal amount of CPKO production for the period January - December 2022 is 7,058,777 kg from the initial target of 5,937. 531 kg.. The total production of CPO and CPKO has no deviation so that the production of CPO and CPKO can be said to be optimal.
Accuracy of the Moving Averages and Deseasonalizing Methods for Trend, Cyclical and Seasonal Data Forecasting Saragih, Yoga Fromega; Darnius, Open
JMEA : Journal of Mathematics Education and Application Vol 2, No 3 (2023): Oktober
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i3.13735

Abstract

Forecasting or forecasting is an attempt to predict future conditions based on past state data. Moving Averages or moving average is a forecasting method that calculates the average value of a time series and then uses it to estimate the value in the next period. Deseasonalizing is part of the decomposition method which is included in the time series method. In this study, the Moving Average method and the Deseasonalizing method were used. The use of these two forecasting methods is to determine the accuracy of the forecasting method which is more accurate and close to the Mean Absolute Error (MAE) and Mean Squared Error (MSE) values. In this study the procedures used were problem identification, problem formulation, observation, data analysis and conclusion. The data taken in this study is data that contains trend, cyclical, and seasonal. For data containing trends on the moving averages method 15245.28 and 1430419308, for the Deseasonalizing method 28121.9504 and 1204814887. For Cyclical data on the Moving Averages method 4454.314465 and 28200197.22 for the Deseasonalizing method 13357.71283 and 254833253.4. For Seasonal data on Moving Averages 126.3839286 and 25479.38393 for the Deseasonalizing method 244.9971767 and 75372.32397. And for data containing these three patterns in the Moving Averages method 193.5385 and 65781.02 for the Deseasonalizing method 901.9566 and 1351418. From these results it can be concluded that the most effective trend data is the Deseasonalizing method, for Seasonal data the most effective method is the Moving Averages method, and for Cyclical Data the most effective method is the Moving Averages. Meanwhile, for data containing the three data patterns is the Moving Averages method.

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