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Media Statistika

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ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

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Articles 220 Documents

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ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS Haqiqi, Baiq Nurul; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCMâ€™s disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices.Keywords : Clustering, Fuzzy C-Means, Subtractive Clustering, Subractive Fuzzy C-Means

CREDIT SPREADS PADA REDUCED-FORM MODEL Di Asih I Maruddani; Dedi Rosadi; Gunardi Gunardi; Abdurakhman Abdurakhman
MEDIA STATISTIKA Vol 4, No 1 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

There are two primary types of models in the literature that attempt to describe default processes for debt obligations and other defaultable financial instruments, usually referred to as structural and reduced-form (or intensity) models. Structural models use the evolution of firms’ structural variables, such as asset and debt values, to determine the time of default. Reduced form models do not consider the relation between default and firm value in an explicit manner. Reduced form models assume that the modeler has the same information set as the market - incomplete knowledge of the firm’s condition. that leads to an inaccessible default time. The key distinction between structural and reduced form models is not whether the default time is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology. Credit spreads are used to measure credit premium, which compensates risk-averse investors for assuming credit risk. Therefore, the credit spreads should remain positive. The higher credit risk assumed by the investors, the higher credit premium got be payed by them. In this paper, we have to to determine the credit spreads of reduced-form model. Keywords: Reduced-Form Model, Hazard Rate, Credit Spreads

PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA) Retsi Firda Maulina; Anik Djuraidah; Anang Kurnia
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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%.

INFERENSI STATISTIK DARI DISTRIBUSI NORMAL DENGAN METODE BAYES UNTUK NON-INFORMATIF PRIOR Prahutama, Alan; Sugito, Sugito; Rusgiyono, Agus
MEDIA STATISTIKA Vol 5, No 2 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the method that can be used in statistical inference is Bayesian method. It combine sample distribution and prior distribution to get a posterior distribution. In this paper, sample distribution used is univariate normal distribution. Prior distribution used is non-informative prior. Determination technique of non-informative prior use Jefrreyâ€™s methodÂ from univariate normal distribution. After got the posterior distribution, find theÂ marginal distribution of mean and variance. So that will get the parameter estimation of interval for mean and variance. Hypothesis testing for mean and variance can find from parameter estimation of formed interval. Â Keywords: Bayesian method, non-informatif prior, Jeffreyâ€™s method, Parameter Estimation of Interval, Hypothesis test

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

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 classifyÂ 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.Â Keywords: Classification, Fuzzy Cluster Means, Livestock

Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang) Yasin, Hasbi; Ispriyansti, Dwi
MEDIA STATISTIKA Vol 10, No 1 (2017): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.Keywords:Birthweight, Classification, LBW, WPNN.

PENGUKURAN VALUE AT RISK PADA ASET TUNGGAL DAN PORTOFOLIO DENGAN SIMULASI MONTE CARLO Maruddani, Di Asih I; Purbowati, Ari
MEDIA STATISTIKA Vol 2, No 2 (2009): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is the established standard for measuring market risk. VaR measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level. A VaR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). The Monte Carlo simulation method calculates the change in the value of positions by using a random sample generated by price scenarios. Instead of using the past value of risk factors, Monte Carlo simulation generates models to estimate the risk factors from past portfolio returns by specifying the distributions and their parameters. Using these distributions and parameters, we can generate thousands of hypothetical scenarios for risk factors and, finally, we can determine future prices or rates based on hypothetical scenarios. VaRs can be derived from the cumulative distribution of future prices or rates for given confidence levels. In this paper, we calculate VaR at PT Astra International Tbk., PT Telekomunikasi Tbk., and the portfolio of the two assets. PT. Astra International Tbk has higher VaR than PT. Telekomunikasi Tbk. The VaR of a portfolio has lower result than VaR of each single asset. Â Keywords : Value at Risk, Time Period, Confidence Level, MonteÂ Carlo Simulation.

ANALISIS DAMPAK GUNCANGAN HARGA MINYAK MENTAH TERHADAP MAKROEKONOMI INDONESIA: APLIKASI VECTOR ERROR CORRECTION MECHANISM Michael Andre; Nasrudin Nasrudin
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesian Crude Oil Price (ICP) often fluctuates by the shock of world oil prices. Because of its important role, the fluctuations or shocks in ICP will affect Indonesia's macro economy. To overcome this problem, this study analyzes the impact of the crude oil price shocks on Indonesia's macro economy which includes economic growth and the money supply (M2) during 2010-2016 using Vector Error Correction Mechanism (VECM). The results show that short-term fluctuations of ICP have a significant and positive effect on economic growth but have a non-significant effect on the money supply. In the long term equilibrium, ICP have a positive and significant effect to both economic growth and money supply which in line with Impulse Response Function (IRF) and Decomposition of Variance (FEDV) analysis. Given its positive impact, the recent decline in oil prices will harm the Indonesian economy. Therefore, the government needs to reduce its dependence on crude oil exports and accurately predict the crude oil price in the future.

ANALISIS TINGKAT STRESS WANITA KARIR DALAM PERAN GANDANYA DENGAN REGRESI LOGISTIK ORDINAL (Studi Kasus pada Tenaga Kerja Wanita di RS. Mardi Rahayu Kudus) Nova, Nova; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Currently, the role of women has shifted from traditional to modern roles. From only a traditional role to bear children and run the household, women now have a social role which can be a career with supported higher education. This can result in conflict dual role as worker and housewife for women who have a family, so easy to cause stress. Several factors are thought to affect levels of stress, especially for career women is child care, housekeeping assistance, communication and interaction with children and husband, time for family, determining priorities, career pressures and family pressures, and the husband's view of the dual role of women. Based on the test independence of variables, seven variables have a relationship with the level of stress career woman.By using the likelihood ratio test and Wald test is found to be two factors affect the stress levels of women workers in Mardi Rahayu Kudus hospital are a time for family and the support of her husband in a career. Â Keywords: Stress Level, Dual Role Conflict, Ordinal Logistic Regression, Mardi Rahayu Hospital.

PEMODELAN DATA KEMATIAN BAYI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION Ramadhan, Riza F.; Kurniawan, Robert
MEDIA STATISTIKA Vol 9, No 2 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Overdispersion phenomenon and the influence of location or spatial aspect on data are handled using Binomial Geographically Weighted Regression (GWNBR). GWNBR is the best solution to form a regression analysis that is specific to each observationâ€™s location. The analysis resulted in parameter value which different from one observation to another between location. The Weighting Matrix Selection is done before doing The GWNBR modeling. Different weightingÂ will resulted in different model. Thus this study aims to Â investigate the best fit model using infant mortality data that is produced by some kind of weighting such as fixed kernel Gaussian, fixed kernel Bisquare, adaptive kernel Gaussian and adaptive kernal Bisquare in GWNBR modeling. This region study covers all the districts/municipalities in Java because the number of observations are more numerous and have more diverse characteristics. The study shows that out of four kernel functions, infant mortality data in Java2012, the best fit model is produced by fixed kernel Gaussian function. Besides that GWNBR with fixed kernel Gaussian also shows better result than the poisson regression and negative binomial regression for data modeling on Â infant mortality based on the value of AIC and Deviance.Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Keywords: Â GWNBR, infant mortality, fixed gaussian, fixed bisquare, adaptive gaussian, adaptive bisquare.

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