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<![CDATA[PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA) ]]>
<![CDATA[Retsi Firda Maulina]]>;
<![CDATA[Anik Djuraidah]]>;
<![CDATA[Anang Kurnia]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.140-151
<![CDATA[Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.]]>
<![CDATA[PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA) ]]>
<![CDATA[Herni Utami]]>;
<![CDATA[Yunita Wulan Sari]]>;
<![CDATA[Subanar Subanar]]>;
<![CDATA[Abdurakhman Abdurakhman]]>;
<![CDATA[Gunardi Gunardi]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.214-225
<![CDATA[This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.]]>
<![CDATA[PERAMALAN DATA PENUMPANG KERETA API DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN) ]]>
<![CDATA[Mira Andriyani]]>;
<![CDATA[Subanar Subanar]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.164-174
<![CDATA[The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of passengers at the station so that it sometimes causes a accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result thanS ARIMA and RNN.]]>
<![CDATA[PENENTUAN SEBARAN SPASIAL PENCEMARAN AIR DI KOTA PONTIANAK MENGGUNAKAN ANALISIS DISKRIMINAN DUA KELOMPOK ]]>
<![CDATA[Muhammad Fikri]]>;
<![CDATA[Naomi Nessyana Debataraja]]>;
<![CDATA[Dadan Kusnandar]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.226-235
<![CDATA[Clean water is one point of sustainable Development Goals (SDGs), so to keep indicator water quality must be determine every period. The Discriminant analysis is an analysis of dependence that is used to classify objects into several categories. The purpose of this study were to determine the discriminant model that consist of dominant factors of water pollution. Samples were taken from 42 locations in the surrounding area of Pontianak City. The sample were analyzed in the laboratory for the contents of ferrum (Fe), dissolved oxygen (DO), biochemical oxygen demand (BOD). Width of the river were also considered is an independent variable. The methodology includes determining the pollution index that will be used as dependent variable, testing the assumption of multivariate normality and similarity of the covariance variance, conducting the discriminant analysis classification process using the Apparent Error Rate method. The pollution level of each location was visualized in a map. The resulting discriminant model has an accuracy rate of 69%.]]>
<![CDATA[VALUE AT RISK IN STOCK PORTFOLIO USING T-COPULA: Case Study of PT. Indofood Sukses Makmur, Tbk. and Bank Mandiri (Persero), Tbk. ]]>
<![CDATA[Qorina Rara Sartika]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Moch Abdul Mukid]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.175-187
<![CDATA[Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst losses that occur in the stock portfolio with a certain level of confidence and in certain period of time. In general, financial data has a high volatility value, which is caused the variance of residual model is not constant and nonnormally distributed. In this case, Copula-GARCH can be used to calculate the VaR. The Generalized Autoregressive Conditional Heterocedasticity (GARCH) model can resolve the time series models that have non-constant residual variance. This research use the t-Copula to model the dependency structure in the combined distribution of stock returns. The t-copula function is good in terms of reaching the extreme value state that often occurs in the financial data of stock returns and has heavytails. The empirical data uses the stock return data of PT. Indofood Sukses Makmur, Tbk (INDF) and Bank Mandiri (Persero) Tbk (BMRI) in the period of October 8, 2012 - October 8, 2017. In this research, Value at Risk is calculated using the period 1 day ahead at 90% confidence level that is 0.042, at 95% confidence level that is 0.025 and at 99% confidence level that is 0.017 with weight of each stock is 50%.]]>
<![CDATA[MODEL DEBIT DAERAH ALIRAN SUNGAI JANGKOK BERDASARKAN HASIL PREDIKSI MODEL STATISTICAL DOWNSCALING NONPARAMETRIK KERNEL CURAH HUJAN DAN TEMPERATUR ]]>
<![CDATA[Mustika Hadijati]]>;
<![CDATA[Irwansyah Irwansyah]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.236-245
<![CDATA[River water discharge is influenced by climatic conditions. River water discharge is important information for water resources management planning, so it is necessary to develop river water discharge model as basis of its predictions. In order to get the result of predictions of river water discharge with high accuracy, it is developed a model of river water discharge based on the predictions of local climate (local rainfall and temperature) that are influenced by global climate conditions..Prediction of local climate is based on the Kernel nonparametric statistical downscaling model by utilizing GCM data. GCM data is a high dimensional global data, so data pre-processing is needed to reduce data dimension. It is done by CART algoritm. Statistical downscaling model is used to predict local rainfall and temperature. The prediction results are quite good with relatively small RMSE value. They are used to develop model of river water discharge. Modeling river water discharge is carried out using the Kernel nonparametric approach. The model of river water discharge produced is quite good because it can be used to predict river water discharge with relatively small RMSE.]]>
<![CDATA[MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL ]]>
<![CDATA[Arief Rachman Hakim]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.152-163
<![CDATA[Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.]]>
<![CDATA[ANALISIS KEMISKINAN DI KABUPATEN MALUKU TENGGARA BARAT MENGGUNAKAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) ]]>
<![CDATA[Ferry Kondo Lembang]]>;
<![CDATA[Henry Willyam Michel Patty]]>;
<![CDATA[Feros Maitimu]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.188-199
<![CDATA[Poverty is a condition where there is a condition where there is an inability of the community to meet basic needs such as food, clothing, shelter, education and health. MTB regency is one of the regions in Moluccas Province with a relatively high percentage of the poor population reaching 28.31%. The purpose of this study is to conduct poverty analysis in MTB using the MARS method. The problem of poverty is thought to be very much influenced by many factors, therefore the selection of the MARS method is considered very appropriate because it has the advantage of being able analyze high-dimensional data. The results showed the best MARS model was a combination BF=18, MI=3 and MO=0 with a minimum GCV value at 69.587. Variables that have a significant effect are the percentage RTM that do not have public toilet facilities (X5), the variable percentage of RTM that is the type of floor of a residential building made of poor quality soil / bamboo / wood (X4), and the percentage of RTM that does not own the building (X1).]]>
<![CDATA[MODELING CENTRAL JAVA INFLATION AND GRDP RATE USING SPLINE TRUNCATED BIRESPON REGRESSION AND BIRESPON LINEAR MODEL ]]>
<![CDATA[Suparti Suparti]]>;
<![CDATA[Alan Prahutama]]>;
<![CDATA[Agus Rusgiyono]]>;
<![CDATA[Sudargo Sudargo]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.129-139
<![CDATA[Inflation and Gross Regional Domestic Income (GRDP) are two macroeconomic variables of a region that are correlated with each other. GRDP prices constant (real) can be used as an indicator of economic growth in a region from year to year. Inflation is calculated from the CPI rate and economic growth is calculated from the GRDP rate. Inflation and economic growth in an area are influenced by several factors including bank interest rates. Analysis of data consisting of 2 correlated responses can be performed with birespon regression analysis. In this research, modeling of inflation data and the rate of GRDP through birespon data modeling uses spline truncated nonparametric method and birespon linear parametric method. The purpose of this study is to model inflation data and the Central Java GRDP rate using spline truncated birespon regression. The results are compared with the birespon linear regression model. By using quarterly data from the first quarter of 2007 - the second quarter of 2019, the spline truncated model is better than the linear model, because the spline truncated model has a smaller MSE and R2 is greater than the linear model. Both models have the same performance which is quite good.]]>
<![CDATA[HAZARD PROPORTIONAL REGRESSION STUDY TO DETERMINE STROKE RISK FACTORS USING BRESLOW METHOD ]]>
<![CDATA[Sudarno Sudarno]]>;
<![CDATA[Eri Setiani]]>
<![CDATA[ MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.12.2.200-213
<![CDATA[Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular be death. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. The objectives of this research are applied Cox proportional hazard regression on ties event using Breslow methodand determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. The response variable is length of stay at hospital, and the predictors are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and body mass index. The factors cause stroke disease by significant are type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, and blood sugar level. By the survivorship function that the patients have been looked after at hospital greater than 20 days, they have probability of healthy be little even go to death. A person in order to be healthy must notice and prevent some factors cause disease.]]>
