cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 271 Documents
 16   17   18   19   20 
Front Matter Vol. 11 No. 2 2018 Statistika, Media
MEDIA STATISTIKA Vol 11, No 2 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cover dan Daftar Isi Media Statistika Vol. 11 No. 2 Desember 2018
IDENTIFICATION OF RAINFALL DISTRIBUTION IN WEST SUMATERA AND ASSESSMENT OF ITS PARAMETERS USING BAYES METHOD Yanuar, Ferra; Sari, Putri Trisna; Asdi, Yudiantri
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.161-169

Abstract

One distribution of rainfall data is a lognormal distribution with location parameters  and scale parameters . This study aims to estimate the mean and variance of rainfall data in several selected cities and regencies in West Sumatra. Parameter estimation is estimated by using maximum likelihood estimation (direct method) and Bayes method. This study resulted that the Bayes method produces a better predictive value with a smaller variance value than with direct estimation. It was concluded that the estimation by the Bayes method was a better estimator method than the direct estimation.
STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA Supandi, Epha Diana
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.136-148

Abstract

Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.
ANALYSIS OF SRONDOL-JATINGALEH TOLL QUEUE SYSTEM AT SEMARANG CITY IN THE END OF YEAR 2018 WITH AUTOMATIC TOLL GATE SYSTEM USING LOGISTIC DISTRIBUTION APPROACH Sugito, Sugito; Prahutama, Alan
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.218-224

Abstract

The transportation sector is one sector that plays an essential role in economic growth. The transportation sector can increase economic growth. Semarang City is one of the provincial capitals in Central Java. The Srondol-Jatingaleh toll road is one of the toll roads in the city of Semarang that has implemented the Automatic Toll Gate. Based on the results of the analysis, so that the queue model is (logistic/logistic/ 4) :( FIFO / ∞ / ∞). It shows that the distribution of the queuing system of the number of arrivals and the number of vehicle services are Logistic-Distribution. The number of service facilities is 4, the service discipline used is First In First Out (FIFO), the size in the queue, and the source of calls are unlimited.
Front Matter Vol. 12 No. 1 2019 Statistika, Media
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cover dan Daftar Isi Vol. 12 No. 1 Juni 2019
SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING Hermansah, Hermansah; Rosadi, Dedi; Abdurakhman, Abdurakhman; Utami, Herni
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.116-124

Abstract

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
A SIMULATION STUDY OF FIXED-B ASYMPTOTIC DISTRIBUTIONS IN LINEAR PANEL MODELS WITH FIXED EFFECTS Setyowati, Indah Rini; Notodiputro, Khairil Anwar; Kurnia, Anang
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.206-217

Abstract

In linear models, panel data often violates the assumption that the error terms should be independent. As a result, the estimated variance is usually large and the standard inferential methods are not appropriate. The previous research developed an inference method to solve this problem using a variance estimator namely the Heteroskedasticity Autocorrelation Consistent of the Cross-Section Averages (HACSC), with some improvements. The test statistic of this method converges to the fixed-b asymptotic distribution. In this paper, the performance of the proposed inferential method is evaluated by means of simulation and compared with the standard method using plm package in R. Several comparisons regarding the Type I Error of these two methods have been carried out. The results showed that the statistical inference based on fixed-b asymptotic distribution out-perform the standard method, especially for the panel data with small number of individual and time dimension.
FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD Atmaja, Dinul Darma; Widowati, Widowati; Warsito, Budi
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.98-107

Abstract

Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.
AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA Puspita Kartikasari; Hasbi Yasin; Di Asih I Maruddani
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.44-55

Abstract

Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.
UTILIZATION OF STUDENT’S T DISTRIBUTION TO HANDLE OUTLIERS IN TECHNICAL EFFICIENCY MEASUREMENT Zulkarnain, Rizky; Djuraidah, Anik; Sumertajaya, I Made; Indahwati, Indahwati
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.56-67

Abstract

Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Student’s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.

Page 18 of 28 | Total Record : 271

 16   17   18   19   20 

Filter by Year

2008 2025


Reset
Filter By Issues
All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue