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INDONESIA
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 8, No 1 (2015): Media Statistika" : 6 Documents clear
ANALISIS SPASIAL PENGARUH TINGKAT PENGANGGURAN TERHADAP KEMISKINAN DI INDONESIA (Studi Kasus Provinsi Jawa Tengah) Rahmawati, Rita; Safitri, Diah; Fairuzdhiya, Octafinnanda Ummu
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (507.192 KB) | DOI: 10.14710/medstat.8.1.23-30

Abstract

Poverty is still being one of big problems in Indonesia. Any efforts are done to find a solution for this problem. Poverty itself can be caused of the high unemployment that occurs. With a number of unemployment, it will be lower income thus reducing also purchasing power and the ability to meet the needs of life thus causing poverty. This study analyzed the impact of unemployment to the poverty as involving spatial factors, using spatial regression analysis. Used data on poverty and unemployment in each regency in the central java, the analysis shows that based on likelihood ratio test, obtained LR test value 6,038 or p-value 0,014001 which means there is a spatial correlation. By testing model simultaneously nor individually using Breusch-Pagan test and Wald test, it show that both are significant, with BP = 6,7094; df = 1; p-value = 0,009591 and Wald statistic = 7,0238; p-value = 0,0080434. The results means there are spatial element in the relations between unemployment and poverty in central java so that SEM is more proper used than ordinary linear regression. Keywords: Spatial Error Model (SEM), Spatial Autocorrelation, Spatial Heterogeneity
OPTIMISASI MULTIOBJEKTIF UNTUK PEMBENTUKAN PORTOFOLIO Hoyyi, Abdul; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (374.978 KB) | DOI: 10.14710/medstat.8.1.31-39

Abstract

Investing in asset such as stock; besides generate profit (return), it is also deal with a risk of loss, so that portofolio diversification is needed to reduce the risk. In the establishment of stock portofolio, the investors seeking to maximize the expected return of investment with a certain level of risk that still can be accepted. Portofolios that can achieve the above objectives called optimal portofolios. The application of multiobjective optimization on the establishment of the optimal portofolio is to maximize the return and minimize the risk at the same time. The aim of this research is to analize the proportion of each stock in order to form an optimal portofolio and to analyze the level of benefits and risks of the portofolio which is formed in accordance with the preferences of investors. The data used are monthly stock data of ASII, TLKM, SMGR, LPKR and BBNI. The optimal portofolio for risk seeker investors is a portofolio that used coefficient  k =0,01, namely by investing in SMGR whilst the optimal portofolio for risk indifference investors is a portfolia which has coefficient 1 ≤ k ≤ 100 namely by investing in ASII, TLKM, SMGR, LPKR, and BBNI. Whereas, the optimal portofolio for risk averse investors is a portfolio which has coefficient k =1000 that is by investing in ASII, TLKM, SMGR, LPKR, and BBNI. Keywords: Portofolio, Multi Objective Optimization
PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA Saptyani, Marita; Sulandari, Winita; Pangadi, Pangadi
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (541.279 KB) | DOI: 10.14710/medstat.8.1.41-48

Abstract

Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecast  336 next period. Keywords: DSARIMA, MAPE, Electricity, Bawen
ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER Suparti, Suparti; Sa'adah, Alfi Faridatus
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (553.653 KB) | DOI: 10.14710/medstat.8.1.1-11

Abstract

The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value. Keywords: Inflation, ARIMA, Outlier, MSE
MODEL EKSPONENSIAL GANDA PADA PROSES STOKASTIK (STUDI KASUS DI STASIUN PURWOSARI) Sugito, Sugito; Wilandari, Yuciana
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (363.109 KB) | DOI: 10.14710/medstat.8.1.49-58

Abstract

In general, mathematical modeling is divided into two, namely the model of deterministic and stochastic models. On stochastic modeling involves several processes among them are the Poisson process, the process of Bernoulli, Gaussian processes, the process of renewal and other processes. Specifically for the Poisson process often found in modeling queuing theory. At Poisson process there are four kinds of sub model that can be formed that is Double Poisson models, Exponential Poisson models, Poisson Exponential model, and Double Exponential models. In this paper will discuss the Double Exponential model in stochastic processes , specifically for the Poisson process. Analysis was performed on the data arrival time and service time. The model is a model (M / M / c) : ( GD / ~, ~) which is a double exponential model in stochastic processes. Keywords: Double Exponential, Poisson Process, Stochastic Process
MODEL CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN ESTIMASI MOMENT PROBABILITAS TERBOBOTI Rusgiyono, Agus; Wuryandari, Triastuti; Rahmawati, Annisa
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (451.893 KB) | DOI: 10.14710/medstat.8.1.13-22

Abstract

The methods is used to analyze extreme rainfall is the Extreme Value Theory (EVT). One of the approaches of EVT is the Block Maxima (BM) which it follows the distribution of Generalized Extreme Value (GEV). In this study, the dasarian rainfall data of 1990-2013 in the Semarang City is divided based on block monthly and examined in October, November, December, January, February, March and April. The resulted blocks are 24 with 3 observations each block. Parameter shape, location and scale are estimated  Probability Weight Moments (PWM) methodes The result of this study are January has the greatest occurrence chance of extreme value, estimated of parameter shape 0,3840564, location 138,8152989 and scale 68,6067117. In addition, the alleged maximum value of dasarian rainfall obtained in a period of 2, 3, 4, 5 and 6 years are 243,45753 mm, 308,23559 mm, 357,26996 mm, 397,96557 mm and 433,28889 mm respectively. Keywords: Rainfall, Extreme Value Theory, Block Maxima, Generalized Extreme Value, Probability Weight Moments

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