CAUCHY
http://ejournal.uin-malang.ac.id/index.php/Math
<h3>CAUCHY - Jurnal Matematika Murni dan Aplikasi</h3><p><span lang="id"><span class="hps"><strong>p-ISSN: <a href="http://u.lipi.go.id/1255061534" target="_blank">2086-0382</a> | e-ISSN: <a href="http://u.lipi.go.id/1447468580" target="_blank">2477-3344</a></strong></span></span><strong></strong></p><p><strong><span lang="id"><span class="hps"><br /></span></span></strong><span id="result_box" lang="en"> <strong>CAUCHY</strong> <span>is a</span> <span>mathematical journal</span> <span>published</span> <span>twice a year</span> <span>in May</span> <span>and</span> <span>November</span> <span>by</span> <span>the Mathematics</span> Department, F<span>aculty of</span> <span>Science</span> <span>and</span> T<span>echnology, </span><span>Universitas Islam Negeri Maulana Malik Ibrahim Malang<br /></span></span></p><p><span lang="en">We welcome authors for original articles (research), review articles, interesting case reports, special articles illustrations that focus on the <strong>Pure and Applied <strong>Mathematics.</strong></strong></span></p><p>Subjects suitable for publication include, <strong>but are not limited</strong> <strong>to</strong>, the following fields of:</p><ul><li>Fuzzy Systems and its Applications</li><li>Geometry Theories and its Applications</li><li>Graph Theories and its Applications</li><li>Real Analysis and its Applications </li><li>Operation Research and its Applications</li><li>Statistical Theories and its Applications</li><li>Dinamical Systems and its Applications</li><li>Mathematics Modeling and its Applications</li><li>Discrete Mathematics and its Applications</li><li>Computer Mathematics and its Applications</li><li>Mathematics Actuaria and its Applications</li></ul><p><span lang="en"><span>Our journal is indexed on DOAJ; Indonesian Scientific Journal Database (ISJD); WorldCat; OneSearch; Google Scholar.</span></span></p><p><span lang="en"><span><strong>Journal History</strong><br />CAUCHY: Jurnal Matematika Murni dan Aplikasi has several changes and updates in its history as follows: 1). On 2009-2012, the cover color was changed from yellow to purple; 2). On 2013-2015, it used two-column system for the layout; 3). From 2016, it uses one-column layout and each article submitted to CAUCHY has to be written in English.</span></span></p><strong><span lang="en"><span id="result_box" lang="en"><span id="result_box" lang="en"><span> <a href="http://s1153.photobucket.com/user/joehari1/media/new1.gif.html" target="_blank"><img src="http://i1153.photobucket.com/albums/p503/joehari1/new1.gif" alt=" photo new1.gif" border="0" /></a> </span></span></span><span id="result_box" lang="en"><span id="result_box" lang="en"><span>Starting from Vol</span><span>.</span> <span>4 No.</span> <span>2</span> <span>(2016)</span> <span>Cauchy</span> <span>use a new layout template</span></span><br /></span></span></strong><p>Registration and article submission guidelines can be downloaded <a href="https://goo.gl/crrzlC" target="_blank">here</a>.</p><p><span lang="id"><span class="hps"><br /></span></span></p>Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malangen-USCAUCHY2086-0382<p>Authors who publish with this journal agree to the following terms:</p><ol><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol>Front - Matter
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4729
Front - Matter
Copyright (c) 2018 CAUCHY
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2017-11-012017-11-015110.18860/ca.v5i1.4729Preface
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4730
Preface Preface
Copyright (c) 2018 CAUCHY
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2017-11-012017-11-015110.18860/ca.v5i1.4730Back - Matter
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4731
Back - Matter
Copyright (c) 2018 CAUCHY
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2017-11-012017-11-015110.18860/ca.v5i1.4731Applied Hierarchical Cluster Analysis with Average Linkage Algoritm
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/3862
This research was conducted in Sidoarjo District where source of data used from secondary data contained in the book <em>"Kabupaten Sidoarjo Dalam Angka 2016"</em> .In this research the authors chose 12 variables that can represent sub-district characteristics in Sidoarjo. The variable that represents the characteristics of the sub-district consists of four sectors namely geography, education, agriculture and industry. To determine the equitable geographical conditions, education, agriculture and industry each district, it would require an analysis to classify sub-districts based on the sub-district characteristics. Hierarchical cluster analysis is the analytical techniques used to classify or categorize the object of each case into a relatively homogeneous group expressed as a cluster. The results are expected to provide information about dominant sub-district characteristics and non-dominant sub-district characteristics in four sectors based on the results of the cluster is formed.Cindy Cahyaning AstutiRahmania Sri Untari
Copyright (c) 2017 CAUCHY
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2017-11-302017-11-30511710.18860/ca.v5i1.3862Estimation of Geographically Weighted Regression Case Study on Wet Land Paddy Productivities in Tulungagung Regency
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4305
<p>Regression is a method connected independent variable and dependent variable with estimation parameter as an output. Principal problem in this method is its application in spatial data. Geographically Weighted Regression (GWR) method used to solve the problem. GWR is a regression technique that extends the traditional regression framework by allowing the estimation of local rather than global parameters. In other words, GWR runs a regression for each location, instead of a sole regression for the entire study area. The purpose of this research is to analyze the factors influencing wet land paddy productivities in Tulungagung Regency. The methods used in this research is GWR using cross validation bandwidth and weighted by adaptive Gaussian kernel fungtion.This research using 4 variables which are presumed affecting the wet land paddy productivities such as: the rate of rainfall(X<span style="vertical-align: sub;">1</span>), the average cost of fertilizer per hectare(X<span style="vertical-align: sub;">2</span>), the average cost of pestisides per hectare(X<span style="vertical-align: sub;">3</span>) and Allocation of subsidized NPK fertilizer of food crops sub-sector(X<span style="vertical-align: sub;">4</span>). Based on the result, X<span style="vertical-align: sub;">1</span>, X<span style="vertical-align: sub;">2</span>, X<span style="vertical-align: sub;">3</span> and X<span style="vertical-align: sub;">4 </span> has a different effect on each Distric. So, to improve the productivity of wet land paddy in Tulungagung Regency required a special policy based on the GWR model in each distric.</p>Danang Ariyanto
Copyright (c) 2017 CAUCHY
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2017-11-302017-11-305181410.18860/ca.v5i1.4305Geographically Weighted Regression (GWR) Modelling with Weighted Fixed Gaussian Kernel and Queen Contiguity for Dengue Fever Case Data
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4393
<p>Regression analysis is a method for determining the effect of the response and predictor variables, yet simple regression does not consider the different properties in each location. Methods Geographically Weighted Regression (GWR) is a technique point of approach to a simple regression model be weighted regression model. The purpose of this study is to establish a model using Geographically Weighted Regression (GWR) with a weighted Fixed Gaussian Kernel and Queen Contiguity in cases of dengue fever patients and to determine the best weighting between the weighted Euclidean distance as well as the Queen Contiguity based on the value of R2. Results from the study showed that the modeling Geographically Weighted Regression (GWR) with a weighted Fixed Gaussian Kernel showed that all predictor variables affect the number of dengue fever patients, whereas the weighted Queen Contiguity, not all predictor variables affect the dengue fever patients. Based on the value of R2 is known that a weighted Fixed Gaussian Kernel is better used.</p>Grissila Yustisia
Copyright (c) 2017 CAUCHY
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2017-11-302017-11-3051151910.18860/ca.v5i1.4393Local Stability Analysis of an SVIR Epidemic Model
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4388
In this paper, we present an SVIR epidemic model with deadly deseases. Initially the basic formulation of the model is presented. Two equilibrium point exists for the system; disease free and endemic equilibrium. The local stability of the disease free and endemic equilibrium exists when the basic reproduction number less or greater than unity, respectively. If the value of R0 less than one then the desease free equilibrium is locally stable, and if its exceeds, the endemic equilibrium is locally stable. The numerical results are presented for illustration.Joko Harianto
Copyright (c) 2017 CAUCHY
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2017-11-302017-11-3051202810.18860/ca.v5i1.4388Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4288
The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Y<sub>t</sub>) sequence expected to be effected by an input series (X<sub>t</sub>) and other inputs in a group called a noise series (N<sub>t</sub>). Multi input transfer function model obtained is (<em>b<sub>1</sub>,s<sub>1</sub>,r<sub>1</sub></em>) (<em>b<sub>2</sub>,s<sub>2</sub>,r<sub>2</sub></em>) (<em>b<sub>3</sub>,s<sub>3</sub>,r<sub>3</sub></em>) (<em>b<sub>4</sub>,s<sub>4</sub>,r<sub>4</sub></em>)(<em>p<sub>n</sub>,q<sub>n</sub></em>) = (0,0,0) (23,0,0) (1,2,0) (0,0,0) ([5,8],2) and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.Priska Arindya Purnama
Copyright (c) 2017 CAUCHY
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2017-11-302017-11-3051293510.18860/ca.v5i1.4288The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance
http://ejournal.uin-malang.ac.id/index.php/Math/article/view/4209
<div><p class="Keywords">The purpose of this article was to describe the ability of the quantile regression method in overcoming the violation of classical assumptions. The classical assumptions that are violated in this study are variations of non-homogeneous error or heteroscedasticity. To achieve this goal, the simulated data generated with the design of certain data distribution. This study did a comparison between the models resulting from the use of the ordinary least squares and the quantile regression method to the same simulated data. Consistency of both methods was compared with conducting simulation studies as well. This study proved that the quantile regression method had standard error, confidence interval width and mean square error (MSE) value smaller than the ordinary least squares method. Thus it can be concluded that the quantile regression method is able to solve the problem of heteroscedasticity and produce better model than the ordinary least squares. In addition the ordinary least squares is not able to solve the problem of heteroscedasticity.</p></div>Ferra Yanuar
Copyright (c) 2017 CAUCHY - JURNAL MATEMATIKA MURNI DAN APLIKASI
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2017-11-302017-11-3051364110.18860/ca.v5i1.4209