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Unnes Journal of Mathematics
Published by Universitas Negeri Semarang
ISSN : 22526943     EISSN : 24605859     DOI : https://doi.org/10.15294/ujm
Core Subject : Education,
Mathematics
Unnes Journal of Mathematics (UJM) publishes research issues on mathematics and its apllication. The UJM processes manuscripts resulted from a research in mathematics and its application scope, which includes. The scopes include research in: 1. Algebra 2. Analysis 3. Discrete Mathematics and Graph Theory 3. Differential Equation 4. Geometry 5. Mathematics Computation, 6. Statistics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 11 Documents
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Search results for , issue "Vol 5 No 1 (2016)" : 11 Documents clear
MODEL EPIDEMI SIRS STOKASTIK DENGANSTUDI KASUS INFLUENZA Nurlazuardini, Novia Nilam; Kharis, Muhammad; Hendikawati, Putriaji
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13101

Abstract

Epidemic SIRS model is an epidemic model which illustrate the spread of disease from suscept to infected, and then become a recovered and become suscept again depend of the immunity. In this article, we dicussed epidemic stochastics SIRS model with embedding epidemic deterministic model, analysis of the model and the behavior of this disease in the future. To obtain the result of basic reproduction ration, Crump-Mode-Jagers process with embedding BGW branching process in some process. From the analysis and the simulation of the model were obtained , if 𝑅0 < 1 then the epidemic is extinct and if 𝑅0 ≥ 1 the epidemic is occurred. To illustrate the model simulation were carried out using Maple software. The model simulation give the same result with the analysis.
ANALISIS PERBANDINGAN MENGGUNAKAN ARIMA DAN BOOTSTRAP PADA PERAMALAN NILAI EKSPOR INDONESIA Cynthia, Ari; Sugiman, Sugiman; Zaenuri, Zaenuri
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13102

Abstract

In this research used the data export value of Indonesia as a case study. Indonesia's export would be predicted using ARIMA and bootstrap methods with the help of the program R 2.11.1. Bootstrap method used is bootstrap the ARIMA process. ARIMA method is one of the most common methods used in modeling of time series. However on certain data time series models can not guarantee the fulfillment of the assumptions in the classical statistical analysis. Bootstrap methods can be used in situations where standard assumptions are not met. The main objective of this study is to compare the methods ARIMA and bootstrap the Indonesian export data so as to obtain the best forecasting method that will be used to forecast the data export value of Indonesia for the next period. Based on the results of the two models forecasting, it would have been the result of forecasting that has the smallest value of standard error and approach the original data. Results forecasting export value of Indonesia on ARIMA (1,1,2) has the smallest value and the standard error tends to approach the original data when compared to bootstrap the process models ARIMA (1,1,2). Then ARIMA method is the best forecasting method. Next will be forecasting for the months of April to December 2015 using ARIMA method as the best method.
PEMODELAN GENERALIZED POISSON REGRESSION (GPR) UNTUK MENGATASI PELANGGARAN EQUIDISPERSI PADA REGRESI POISSON KASUS CAMPAK DI KOTA SEMARANG TAHUN 2013 Ruliana, Ruliana; Hendikawati, Putriaji; Agoestanto, Arief
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13103

Abstract

The measles the Semarang experience fluctuates every year, so that the City Health Agency (DKK) Semarang put special attention to reducing many cases measles.In the case of smallpox semarang 2013 was data discrete Poisson. Regression Poisson is nonlinear regression used to analyze data count variable response Poisson and meet the equidispersi. In practice often occurs in violation of discrete overdispersi analysis of data in regression poisson underdispersi and models or improper use.To anticipate such violation used Generalized Poisson Regression in modeling (GPR) data. In this research are variable response used in the case of smallpox Semarang 2013 and variable prediktor used is many medicines measles, community health centers, the poverty and overcrowding every subdistrict across Semarang town. The best model Generalized Poisson Regression (GPR) was gotten.
METODE LEAST TRIMMED SQUARE (LTS) DAN MM-ESTIMATION UNTUK MENGESTIMASI PARAMETER REGRESI KETIKA TERDAPAT OUTLIER Dewi, Elok Tri Kusuma; Agoestanto, Arief; Sunarmi, Sunarmi
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13104

Abstract

This article discusses the theoretical study and use excel and SPSS 19 application of Least Trimmed Square (LTS) methods and MM-estimation methods. Theoretical study focused on the elaboration of the concept of outlier, least trimmed square methods and MM-estimation methods and selection best model use the criteria R2 and resid value. Outlier is data on who did not attend a pattern common regression on the model produced, or not follow as a pattern data as a whole. The existence of outlier in the data can be disrupt the process of data analysis, that led to the data on residual and variance become larger. This research aims to know the effectiveness of robust regression method with Least Trimmed Square (LTS) and MM-estimation in multiple linear regression. This data consisting of age (X1) and body mass index (X2) as variable independent while systolik blood pressure (Y) as dependent variables. The model produced using Least Trimmed Square methods that is Y^=67.141+0.649X1+0.587X2. Regarding the resulting uses the method MM-estimation that is Y^=65.308+0.666X1+0.618X2. Because at Least Trimmed Square method (LTS) obtained the R2 value of is bigger and smaller than the residual method of MM-estimation then it can be concluded that the method of Least Square Trimmed (LTS) is more efficient in the estimate parameter of the regression compared the methods of MMestimation
IMPLEMENTASI METODE AHP-WP PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU TELADAN (Studi Kasus: Yayasan Abadiyah Kuryokalangan) Rifan, Slamet; Arini, Florentina Yuni; Alamsyah, Alamsyah
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13106

Abstract

This research proposed an application of MADM (Multi Attribute Decision Making) in the problems of the exemplary teacher selection with assessment criteria: the preparation of teaching material, lesson plan, application of learning, mastery of the learning material, the use of learning resource/media and the discipline in teaching. In this research, AHP (Analytical Hierarchy Process) method’s was used for weighting the criteria and testing the consistency toward pairwise comparison matrix. If the matrix has been consistent then it can be continued to WP (Weighted Product) method’s process in implementing rank to determine the best alternative. The methods of this research include preliminary studies, data collection phase, system development phase and conclusion. From the results of the ranking in this system, it was found that the teacher got the highest score was teacher with code of teacher: A05 with score 0.0195981, so that the teacher was a recommendation teacher that was selected as an exemplary teacher.
METODE FUZZY TOPSIS MADM SEBAGAI ALTERNATIF PENGAMBILAN KEPUTUSAN MENENTUKAN PENERIMA BEASISWA PPA BERBASIS WEB Halim, Bravura Candra; Alamsyah, Alamsyah; Sugiman, Sugiman
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13107

Abstract

This study examines the decision making to determine the scholarship recipients Improving Academic Achievement (PPA) at the State University of Semarang (UNNES) with details of criteria that include student GPA, number of credits taken, the value of student affairs, and the income of parents. The decision making process admission PPA UNNES scholarship implemented with the programming language of PHP with MySQL database and calculations using the method of Fuzzy TOPSIS Multiple-Attribute Decision Making (MADM). The results of reception system PPA UNNES scholarship in this study is a ranking by 50 students of the value of the preferences of the applicants and then taken by students who ranked top 10 of the results of ranking of preferences for recommended escapes in scholarship acceptance PPA UNNES. Based on these results it can be concluded that a web-based decision support systems can be built using TOPSIS Fuzzy MADM with the structure of the programming language PHP and MySQL as a Database Management System (DBMS). In the development of future systems, can be done by adding other data supporting the selection of scholarship PPA
PERBANDINGAN AKURASI MODEL ARCH DAN GARCH PADA PERAMALAN HARGA SAHAM BERBANTUAN MATLAB Sunarti, Sunarti; Mariani, Scolastika; Sugiman, Sugiman
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13108

Abstract

This article aims to get model data stock Unilever Indonesia Tbk. use the model ARCH and GARCH as well as comparing forecasting accuracy of the result of the next five days ahead model ARCH and GARCH on the stock Unilever Indonesia Tbk. use MATLAB. The methods used are design application forecasting uses GUI MATLAB, next model ARIMA Box-Jenkins, identification ARCH effect, forecasting use the model ARCH and GARCH, and compares the results second forecasting model that is based on the value of RMSE. On residual ARIMA best namely ARIMA(1,1,1) detected the effects ARCH so that data can modeled ARCH and GARCH. Model ARCH and GARCH best respectively namely ARCH(3) and GARCH(1,1). Based on value RMSE be seen that model best for forecasting the next five days ahead of data Unilever Indonesia Tbk. produced bymodels GARCH(1,1) because it has value RMSE smallest with equation conditional mean and conditional variance
ANALISIS VOLATILITY FORECASTING SEMBILAN BAHAN POKOK MENGGUNAKAN METODE GARCH DENGAN PROGRAM R Larasati, Enggar Niken; Hendikawati, Putriaji; Zaenuri, Zaenuri
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13109

Abstract

commodities, namely oil, sugar, eggs, flour, rice, chili, milk, onions, and chicken with GARCH models using the program R 2.11.1.The first step is, to test the data stationary nine basic commodities price increases, stationary data already analyzed using ARIMA method. From the analysis using ARIMA, were estimated several models. To determine the best ARIMA model, conducted a comparison of some of the models that have been in the estimation of the model are then selected by a significant parameter value, the value of ^ 2 is the smallest, smallest AIC value and the largest value of log likelihood. The residual value of the best ARIMA model that will be used to determine the GARCH model to the data of nine price increases of basic commodities. Having obtained the best GARCH model, it will be forecasting the smallest value of standard error and approach the original data.Forecasting results on nine basic price increase in 2015 with the best ie GARCH GARCH (1,1) for the rise in oil prices, chili, onions, chicken and wheat flour, GARCH (2,1) for the price of sugar, milk, rice and eggs. Garch best models have a standard error values are smaller and tend to be closer to the original data. By using the GARCH method, it will be forecasting the rise in prices of daily necessities in 2015.
ANALISIS MODEL THRESHOLD GARCH DAN MODEL EXPONENTIAL GARCH PADA PERAMALAN IHSG Susanti, Susanti; Zaenuri, Zaenuri; Mariani, Scolastika
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13111

Abstract

The purpose of this research were to know (1) the best model among TGARCH model and EGARCH model on predicting JCI value in BEI (2) the results forecasting JCI value in BEI using the best model for a few days later. This reseacrh focused on analysis of TGARCH and EGARCH models in forecasting JCI value. Procedure which used in this research were formulate problem, collecting data, analysis data dan conclusion. Data collected with documentation method that is collected secondary data and literature. Software EVIEWS 6 used as a analysis tool of JCI data. This research result in conclusions that is (1) The best model among models TGARCH and EGARCH models on predicting JCI value in BEI is TGARCH model (2) The results forecasting JCI value in BEI use TGARCH model for day 42th is 5112.81 and for day 43th until day 50th obtained 5112.82 (constant).
MODEL LINEAR GOAL PROGRAMMING PADA PENJADWALAN PERAWAT UGD RUMAH SAKIT UMUM DAERAH KOTA SEMARANG Ichsan, Nur; Dwijanto, Dwijanto; Arifudin, Riza
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13114

Abstract

Scheduling of emergency room is quite a complicated issue for each hospital, including the RSUD Kota Semarang. Many methods can be used in the scheduling of nurses, including the Linear Goal Programming (LGP). The purpose of this study was to determine the form of the LGP model and comparison between manual scheduling and LGP model. The method used in this study is by changing the scheduling rules into a form of LGP models, then the model is solved with LINGO 14. In this model, the hospital rules should not be violated is called major constraints, while the rules may be violated at any time is called additional constraints. Additional constraints is a policy made by the head nurse to schedule in accordance with the conditions and needs of nurses. In this study there are 7 major constraints and 5 additional constraints. Based on calculations, obtained the nurse schedule for 31, 30, 29, and 28 days. From the analysis, LGP schedule for 31 days, 30 days, 29 days, and 28 days to meet all of the major constraints and additional constraints. While in manual schedule, the main constraints are met as much as 5 constraints. Then for the additional constraints are not met for all. From the analysis, LGP models can also be applied to the scheduling of nurses in the other room.

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