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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

Arjuna Subject : -

Articles 11 Documents

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Search results for , issue "Vol 12, No 1 (2019): Media Statistika" : 11 Documents clear

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.

EXPECTED SHORTFALL DENGAN SIMULASI MONTE-CARLO UNTUK MENGUKUR RISIKO KERUGIAN PETANI JAGUNG Rita Rahmawati; Agus Rusgiyono; Abdul Hoyyi; Di Asih I Maruddani
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In risk management, risk measurement plays an important role in allocating capital as well as in controlling (and avoiding) worse risk. Estimating the risk value can be done by using a risk measure. The most popular method for evaluating risk is Value at Risk (VaR). But VaR does not fulfill the coherency as a measure of risk effectiveness. In this paper, we propose Expected Shortfall (ES) which has coherency nature. ES is defined as the conditional expectation of losses beyond VaR of the same confidence level over the same holding period. For measuring ES, we use Monte-Carlo Simulation Method. This method is applied for measuring risk that will be faced by corn’s farmers due to the changes in corn prices in Pemalang city. The results show that the ES value is 0.085472 at 95% confidence level and one-month holding period. This number means that a farmer will face 8.5472% of investment as maximum loss exceeding of VaR.

DIAGRAM KENDALI MEWMV DAN MEWMA BERBASIS MODEL TIME SERIES PADA DATA BERAUTOKORELASI: STUDI KASUS GULA KRISTAL PUTIH Novri Suhermi; Retno Puspitaningrum; Agus Suharsono
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this study, we aim to build a multivariate control chart for autocorrelated data. We use MEWMA and MEWMV control charts which are free of normality assumption. Time series model is then applied to tackle autocorrelation problem in the data where the control charts require independence assumption. The real dataset used is the quality characteristics of white crystal sugar, also called gula kristal putih (GKP). There are 3 quality characteristics of GKP, namely moisture (%), color of solution (IU), and grain type (mm). It is considered that these quality characteristics are correlated each other. Our results show that the variability process is out of control where there are 5 observations outside the control limits. Meanwhile the mean process is also out of control. The factors causing the out of control include the workers, the raw materials, the measurement, the machines, and the methods. The process capability indices result in the values less than 1 which means the process is not sufficiently capable.

PERBANDINGAN MODEL CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL SAHAM SYARIAH Veladita Apriyanti; Epha Diana Supandi
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In stock investments, every investor wants to get a high level of return and low risk. The stock price is very volatile and unpredictable, this makes investors have to find solutions in order to get a benefit from this investment. One way is to form a portfolio. A portfolio is a collection of several shares. There are several models for calculating stock portfolios such as CAPM (Capital Asset Pricing Model) and LCAPM (Liquidity Adjusted Capital Asset Pricing Model). The CAPM is a model that describes the relationship between the expected return and risk of investing in a security. The LCAPM is an extension of CAPM by taking into account the liquidity of assets. Data from Jakarta Islamic Index is used to verify the two models. In this case, the empirical results show that the performance of CAPM is better than the LCAPM.

PREDIKSI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS (MSP) Hasbi Yasin; Budi Warsito; Arief Rachman Hakim
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research covers Spatial Extreme Value method application with Max-Stable Process (MSP) approach that will be used to analysis Extreme Rainfall in Semarang city. Extreme value sample are selected by Block Maxima methods, it will be estimated into Spatial Extreme Value form by including location factors. Then it transform to Frechet distribution because it has a heavy tail pattern. Max Stable Process (MSP) is an extension of the multivariate extreme value distribution into infinite dimension of the Extreme Value Theory. After the best model of extreme rainfall data in Semarang is obtained, then calculated the prediction of extreme rainfall with a certain time period. Predictions are calculated using a return level, predictions of extreme rainfall using the return period of the next two years, at the Semarang City Climatology Station predicted to be a maximum of 100.7539 mm. At the Tanjung Mas Rain Monitoring Station it is predicted that a maximum of 100.1052 mm, Ahmad Yani Rain Monitoring Station is predicted to be a maximum of 109.9379 mm. Maximum prediction of extreme rainfall can also be calculated for future t (time) periods.

PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE Mutik Dian Prabaning Tyas; Di Asih I Maruddani; Rita Rahmawati
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.

ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI Yogo Aryo Jatmiko; Septiadi Padmadisastra; Anna Chadidjah
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.

KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART) Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid; Tarno Tarno
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.

PERAMALAN CURAH HUJAN EKSTRIM DI PROVINSI BANTEN DENGAN MODEL EKSTRIM SPASIAL Anik Djuraidah; Cici Suheni; Banan Nabila
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.

METODE DIAGONALLY WEIGHTED LEAST SQUARE (DWLS) PADA STRUCTURAL EQUATION MODELLING UNTUK DATA ORDINAL: STUDI KASUS DARI PENGGUNA JASA KERETA API MAJAPAHIT MALANG – PASAR SENEN Isnayanti Isnayanti; Abdurakhman Abdurakhman
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Structural Equation Modelling (SEM) is used to examine the relationship between complex variables to obtain a comprehensive picture of the overall model. The basic assumptions in SEM are continuous data types and multivariate normality distributed. But in some studies on social sciences, educational sciences, and medical sciences, the data used usually comes from ordinal variables in the form of a Likert scale which causes data to be not multivariate normal distribution. Diagonally Weighted Least Square (DWLS) is one method that can be used to overcome this problem. In this paper, ordinal data analysis will be conducted on SEM using polychoric correlation data with the DWLS method to compare the results of the suitability of the model with the Maximum Likelihood (ML) method. The discussion is complemented by a case study of the effect of service quality on customer satisfaction and loyalty of Majapahit Railway service in Malang-Pasar Senen.The results showed that the proposed model fit after modification model based on the criteria of 'goodness of fit' with chi-square value T=15.24, P-value=0.5785, RMSEA=0.000, GFI=0.99, AGFI=0.97, NNFI =1.03, CFI=1.00, PNFI=0.53.

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