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Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 7, No 2 (2014): Media Statistika" : 6 Documents clear
ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS Wilandari, Yuciana; Mukid, Moch. Abdul; Megawati, Nurhikmah; Sutarno, Yulia Agnis
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.834 KB) | DOI: 10.14710/medstat.7.2.77-88

Abstract

One of the fundamental problems that always exist in a regions in Indonesia is the problem of poverty. Various poverty reduction efforts initiated by the Central Government and the Regions is now experiencing growth and significant shifts in accordance with the direction and context of poverty reduction targets. To overcome poverty, one of the things done by the Central Java provincial government is to help livestock. Livestock types cultivated in Central Java, is a large livestock, namely cattle (beef / dairy), buffalo and horses, while small livestock consists of goats, sheep and pigs. For that conducted the study to classify  cities in Central Java into groups based on livestock population. The grouping using fuzzy cluster analysis means. From this study showed that of the three kinds of clusters obtained many tried to do the most accurate cluster is 3 clusters with Xie-Beni index 0,3279177, with cluster 1 are 20 city, cluster 2 are 12 City and cluster 3 there are 3 City. Keywords: Classification, Fuzzy Cluster Means, Livestock
PEMODELAN INFLASI BERDASARKAN HARGA-HARGA PANGAN MENGGUNAKAN SPLINE MULTIVARIABEL Prahutama, Alan; Utama, Tiani Wahyu; Caraka, Rezzy Eko; Zumrohtuliyosi, Dede
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.36 KB) | DOI: 10.14710/medstat.7.2.89-94

Abstract

Inflation is defined as a sustained increase in the general level of price for goods and services. Some of the events that led to inflation in Indonesia is rising fuel prices, rising prices of meat and chili. Inflation has negative impact, because decreased purchasing power.  So that the inflation model is needed. Modeling inflation can be use regression models. The approach can be performed with nonparametric regression, one of method of nonparametric regression is spline method. In this case, use three predictors to modeling inflation using spline multivariable. The predictors are price of rice, price of chicken, and price of chili. Obtained multivariable spline models with R-square of 93.94% with optimal m = 2 (quadratic) for 1 knots. Keywords: Spline Multivariable, GCV, Inflation
PEMODELAN VOLATILITAS UNTUK PENGHITUNGAN VALUE AT RISK (VaR) MENGGUNAKAN FEED FORWARD NEURAL NETWORK DAN ALGORITMA GENETIKA Yasin, Hasbi; Suparti, Suparti
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.333 KB) | DOI: 10.14710/medstat.7.2.53-61

Abstract

High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). This model can serve as a model input in the model Feed Forward Neural Network (FFNN) with Genetic Algorithms as a training algorithm, known as GA-Neuro-GARCH. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a model GA-Neuro-GARCH on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (1,0,1) -GARCH (1,1), so that the model used FFNN consists of 2 units of neurons in the input layer, 5 units of neurons in the hidden layer neuron layer and 1 unit in the output layer. then using a genetic algorithm with crossover probability value of 0.4, was obtained that the Mean Absolute Percentage Error (MAPE) of 0,0039%. Keywords: FFNN, Genetic Algorithm, GARCH, Volatility
PERBANDINGAN KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES) Waluyo, Agung; Mukid, Moch. Abdul; Wuryandari, Triastuti
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.113 KB) | DOI: 10.14710/medstat.7.2.95-104

Abstract

Credit is the greatest asset managed the bank and also the most dominant contributor to the bank’s revenue. Debtors to pay their credit to the bank may smoothly or jammed. This study aims to identify the variables that affect a debtor’s credit status and compare the acuration of classification method both classification and regression trees (CART)  and logistic regression. The variables used were debtor’s gender, education level, occupation, marital status, and income. By using logistic regression, it was known that only the debtor’s income effect their credit status with the classification accuration reach into 80%. By using CART, there were some variables affect the credit status and the classification accuration 80,9%. This paper showed that the performance of CART in classifying the credit status of debtors was better than logistic regression. Keywords: Credit Status, Logistic Regression, CART  
PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA Janah, Sufia Nur; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.422 KB) | DOI: 10.14710/medstat.7.2.63-69

Abstract

Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model.  The hybrid model would rather be used in the short-term forecasting, no more than three periods. Keywords: ARIMA, Backpropagation, Hybrid, Grain Price
RANCANGAN D-OPTIMAL UNTUK MODEL EKSPONENSIAL GENERAL Tatik Widiharih; Sri Haryatmi; Gunardi Gunardi
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.702 KB) | DOI: 10.14710/medstat.7.2.71-76

Abstract

Exponential model is widely used in biology, chemistry, pharmacokinetics and microbiology. D-optimal criteria is criteria with the purpuse to minimize the variance of  the estimator of parameters in the model. In this paper will discuss the D-optimal design for the generalized exponential model with  homoscedastics  errore assumtion. We used minimally supported design with the proportion of  each design point is uniform. The optimization is used  modified Newton, and the results obtained that the  design points are  interior points of the design region. Keywords: D-Optimal, Generalized Exponential, Minimally Supported Design, Support Point, Homoscedastics

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