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All Journal Saintia Matematika Journal of Mathematics Education and Application (JMEA) Sinkron : Jurnal dan Penelitian Teknik Informatika CARTESIUS : Jurnal Pendidikan Matematika Journal of Research in Mathematics Trends and Technology FARABI: Jurnal Matematika dan Pendidikan Matematika Journal of Mathematics Technology and Education Cartesius Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA)
Open Darnius
Universitas Sumatera Utara
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Journal of Mathematics Education and Application (JMEA)

 1 
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.
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.
Accuracy of the Moving Averages and Deseasonalizing Methods for Trend, Cyclical and Seasonal Data Forecasting Yoga Fromega Saragih; Open Darnius
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.
Co-Authors Asima Manurung Elvin Juliani Gulo Fahrul Rozi Harahap Hasoloan M Nababan Henry Rani Sitepu Herlin Simanullang Ide Prasanti Hutagalung Israil Sitepu Kristina Pestaria Sinaga Laksono Trisnantoro Lisna Astria Meili Yanti Monica Natalia Bangun Mutia Sari Nofrida Elly Zendrato Parapat Gultom Pasukat Sembiring Reanty Teresa Aritonang Rifalin Delustia Purba Rifan halomoan tua sinaga Sri Wahyuni Lingga SRI WULANDARI Sutarman Sutarman Sutarman Sutarman Sutarman Sutarman Sutarman Tulus Tulus Ujian Sinulingga Utari Sri Mayanti Widya Hardiyanti Yoga Fromega Saragih Yuni Wardani