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Media Statistika

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Published by Universitas Diponegoro

ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

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Articles 6 Documents

Search results for , issue "Vol 6, No 2 (2013): Media Statistika" : 6 Documents clear

ANALISIS VARIABEL KANONIK BIPLOT UNTUK BANK UMUM DI JAWA TENGAH Yasin, Hasbi; Rusgiyono, Agus
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in Central Java with the Canonical Variate Analysis (CVA) Biplot. This analysis can be applied to find out information about the relative position, the similarity between the object characteristics and diversity of variables in the three groups of commercial banks in Central Java, namely state-owned banks, private banks and private banks Non Foreign Exchange, based on the health aspects of the bank. The results obtained are the banks in each group had different characteristics shown in the relative position of the already well-separated in the resulting biplot. Variables that tend to influence the grouping of commercial banks are Capital Adequacy Ratio (CAR). The total assets is variable with the highest level of prediction accuracy on each bank. Â Keywords: Health Aspects of the Bank, Commercial Banks, Canonical Variate Analysis (CVA) Biplot.

ANALISIS OBYEK DAN KARAKTERISTIK DARI MATRIKS INDIKATOR MENGGUNAKAN HYBRID ANALISIS KELAS LATEN DENGAN BIPLOT ANALISIS KOMPONEN UTAMA (BIPLOT AKU) Ginanjar, Irlandia; Pravitasari, Anindya Apriliyanti; Martuah, Aleknaek
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Analysis of the object and the characteristics will be much easier, efficient, and informative when based on a perceptual map, which can display objects and characteristics. Indicator matrix is a matrix where the rows represent objects and the columns is a dummy variable representing characteristics. This article writes about techniques to make perceptual map from indicator matrix, where that can provide information about the similarity between objects, the diversity of each characteristic, correlations between the characteristics, and characteristic values â€‹â€‹for each object, the techniques we call Hybrid Latent Class Cluster with PCA Biplot, where Latent Class Cluster Analysis is used to transform the indicator matrix to cross section matrix, where rows represent the objects and columns represent the characteristics, the observation cells is the probability of characteristic for each object, next the cross section matrix mapped using Principal Component Analysis Biplot (PCA Biplot). Â Key Words: Hybrid Latent Class Cluster with PCA Biplot, Latent Class Cluster Analysis, Biplot Principal Component Analysis, Indicator Matrix.

ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Suparti, Suparti
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV). By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 canâ€™t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%. Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation

NEW METHOD TO MINING ASSOCIATION RULES USING MULTI-LAYER MATRIX QUADRANT Hakim, R. B. Fajriya
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Successful retail organizations utilizing any information they had for managing sales strategies. Most of information about consumerâ€™s retail organization had been stored in transactions database. Discovering knowledge from information stored in the transaction database has led several established methods implemented in many cases with their advantages and disadvantages. One of methodologies to uncover relationship among frequent items purchased in transaction database known as association rules. However, the research of association rules techniques to find knowledge from transaction database still provides a significant opportunity for new methods to participate. In this paper, we proposed a new method of mapping a frequent item set to a multi-layer matrix quadrant. This new method could show the metrics usually used to describe the association rules between items purchased same as any method used in association rules analysis.Keywords: Association Rules, Matrix Quadrant, Support, Confidence, Lift Ratio

MODEL PREDIKSI CURAH HUJAN DENGAN PENDEKATAN REGRESI PROSES GAUSSIAN (Studi Kasus di Kabupaten Grobogan) Mukid, Moch. Abdul; Sugito, Sugito
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting method of rainfall has developed rapidly, ranging from the deterministic approach to the stochastic one. Deterministic approach is done through an analysis based on physical laws expressed in mathematical form, which identify the relationships between rainfall and temperature, air pressure, humidity and the intensity of solar radiation. Similarly, there are some stochastic models for the prediction of rainfall that have been commonly used, for instances, the model Autoregressive Integrated Moving Average (ARIMA), Fourier analysis and Kalman filter analysis. Some researchers about climate and weather have also developed a predictive model of rainfall based on nonparametric models, especially models based on artificial neural networks. Above models are based on classical statistical approach where the estimation and inference of model parameters only pay attention to the information obtained from the sample and ignore the initial information (prior) of parameter model. In this research, prediction model with Gaussian process regression approach is used for predicting the monthly rainfall. Gaussian process regression uses a stochastic approach by assuming that the amount of rainfall is random. Based on the value of Root Mean Square Error Prediction (RMSEP), the best covariance function that can be used for prediction is Quadratic Exponential ARD (Automatic Relevance Determination) with RMSEP value 123,63. The highest prediction of the monthly rainfall is in January 2014Â reached into 336,5 mm andÂ the lowest in August 2014 with 36,94 mm. Â Key Words: Gaussian Procces Regression, Covariance Function, Rainfall Prediction

APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG Anggraeni, Dian; Prahutama, Alan; Andari, Shofi
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834. Key words: GSTAR, Vehicle Volume, Space-Time Model

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