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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.

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Search results for , issue "Vol 5, No 4 (2016): Jurnal Gaussian" : 23 Documents clear

PEMODELAN NEURO-GARCH PADA RETURN NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA Umi Sulistyorini Adi; Budi Warsito; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate can be defined as the value of a currency against other currencies. Exchange rates always fluctuate all the time. Very high fluctuations and unconstant becoming problem in forecasting where the data changed extremely. Most of economic data have heteroskedasticity characteristic analyzed using (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models. Another model that commonly used as an alternative is Artificial Neural Network (ANN). However, both models have weaknesses. ARIMA models are linear, but the residual probably still contains non-linear relationship, while the ANN model used to non-linear relationship there is difficulty in determining the input. In this research combination of the two models is Neuro-GARCH model, with GARCH model used as input of ANN model. The purpose of this study was determined the best variance model Neuro-GARCH of return exchange rates rupiah against US dollar. The data used is daily return value of the rupiah (IDR) against the US dollar (USD) from August 27th, 2012 to March 31st, 2016. In this research, the mean model obtained is MA (1) and varian model is GARCH (1,1). The best model is Neuro-GARCH (2-10-1) which MSE smaller than the GARCH (1,1). Keywords: exchange rate, return, GARCH, Neuro-GARCH.

ANALISIS KLASTER KECAMATAN DI KABUPATEN SEMARANG BERDASARKAN POTENSI DESA MENGGUNAKAN METODE WARD DAN SINGLE LINKAGE Annisa Nur Fathia; Rita Rahmawati; Tarno Tarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Physical and non-physical aspects are the ways to explain a diversity among regions, including a diversity among districts. Village potential providing data about the existence, availability and development potential of each administrative area. To know the district that has the same characteristics, do the grouping using cluster analysis. Cluster analysis is a grouping of objects or cases into groups smaller where each group contains objects that are similar to one another. Clustering process is done for 19 districts in Semarang Regency by ward’s method and single linkage. Four cluster are chosen for the process of potential developing more specific in each district. From the analysis using ward’s method, 1st cluster obtained with minimal educational facilities. 2nd cluster with minimal health facilities. 3rd cluster with the districts which caracteristics itself have a good condition. 4th cluster with minimal power line facilities. From the analysis using single linkage method, 1st cluster obtained with a good condition of power line facilities. 2nd cluster with a good condition of educational facilities. 3rd cluster with a minimal educational facilities. 4th cluster with minimal power line facilities. R-Squared value from single linkage method is higher than ward’s method, this shows the single linkage clustering method produces cluster features with each other more heterogeneous compared to the clustering method ward. Keywords: Cluster Analysis, Ward’s Method, Single Linkage, Distict, Village Potential.

PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG), JAKARTA ISLAMIC INDEX (JII), DAN HARGA MINYAK DUNIA BRENT CRUDE OIL MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE EXOGENOUS (VARX) Nunung Hanurowati; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Index of stocks listed on the Indonesia Stock Exchange (IDX) there are conventional that one of them is the Composite Stock Price Index (CSPI) and the index of stocks that are sharia is the Jakarta Islamic Index (JII). In its movement, the value of CSPI and JII often increases and decreases that are influenced by several factors, one of which is the world oil price of Brent Crude Oil. To see the value of CSPI and JII conditions during the period of the next few months it takes the model equations. Because the third such data included in the time series data, we used time series analysis with the appropriate method is the Vector Autoregressive Exogenous (VARX). VARX(p,q) is a model of multivariate time series that consists of several endogenous variable of the time series order p with q adding exogenous variables. The purpose of this study is to obtain an appropriate VARX models and forecasting for data CSPI and JII. The model to predict CSPI and JII with exogenous variables that influence the world oil prices of Brent Crude Oil is VARX(1,1). Test parameters for exogenous variables in the model VARX(1,1) not significant at significance level α = 5%, but this result could be ignored and continues to testing residual assumptions. Residual model VARX(1,1) satisfies the assumption of white noise and multivariate normal distribution, in order to obtain results as very good forecast that with each MAPE value for CSPI and JII forecast of 2,71% and 3,63%. Keywords: CPSI, JII, Brent Crude Oil, VARX, MAPE.

PEMODELAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE (SGSTAR) (Studi Kasus: Produksi Padi di Kabupaten Demak, Kabupaten Boyolali, dan Kabupaten Grobogan) Aisha Shaliha Mansoer; Tarno Tarno; Yuciana Wilandari
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generalized Space Time Autoregressive (GSTAR) model is more flexible as a generalization of Space Time Autoregressive (STAR) model which be able to express the linear relationship of time and location. The purpose of this study is to construct GSTAR model for forecasting the rice plant production in the three districts of Central Java. The data which used to contruct the model is quarterly data of rice plant production in Demak, Boyolali and Grobogan from 1987 through 2014. According to the empirical study result using GSTAR model with uniform weight, binary weight, inverse distance wight, and normalized cross correlation weight, GSTAR (31)-I(1)3 with uniform weight is the optimal model. The model shows that every location is influenced by the location itself. Keywords : GSTAR, Space Time, uniform weight

PERAMALAN DAYA LISTRIK BERDASARKAN JUMLAH PELANGGAN PLN MENGGUNAKAN MODEL FUNGSI TRANSFER DENGAN OUTLIER (Studi Kasus di PT PLN (Persero) Rayon Semarang Selatan) Retza Bahtiar Anugrah; Sudarno Sudarno; Budi Warsito
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Electrical energy is one of the components of Gross Domestic Product which able to stimulate the economic matter because it has been becoming a primary needs in the society. In order to meet the growing electrical energy, State-Owned Enterprises (SOEs) need to develop systems and proper planning. It needs a forecasting of electric power based on customer to meet a sufficient electricity supply. This study aims to predict the electrical power by electric customers using transfer function model with outliers. The use of transfer function model is intended to determine the role of power users that have an impact on the electric power. One of the stages of modeling the transfer function is to set the order of the transfer function parameters, they are b, r, and s. And by modeling the outlier is useful to eliminate the effect of outliers itself. The analysis and discussion show that based on the AIC value, the best model is the transfer function model by weighting the impulse response of the parameter that is ω_0 = 55,55652 and the noise series model of the transfer function is ARIMA (1,0,1) with 8 outliers. The details of the outliers consist of one Additive Outliers type in the 33rd and seven Level Shift Outliers in the 14th, 31st, 9th, 10th, 21st, 22nd and 58th. Size forecasting accuracy using MAPE value 19.77%. Keywords: Transfer function, outliers, ARIMA, electrical power, AIC, MAPE

ANALISIS CLUSTER DENGAN ALGORITMA K-MEANS DAN FUZZY C-MEANS CLUSTERING UNTUK PENGELOMPOKAN DATA OBLIGASI KORPORASI Desy Rahmawati Ningrat; Di Asih I Maruddani; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a method of grouping data (object) that are based on information that found in the data which describes the object and relation within. Cluster analysis aims to make the joined objects in the cluster are identical (or related) with one another and different (not related) to objects in another cluster. In this study used two method of grouping; Fuzzy C-Means and K-Means Clustering. The data used in this research had been using 357 corporate bonds data on December 1st, 2015. The variables used in this study consist of coupon rate, time to maturity, yield and rating of each corporate. The determination of the number of optimum clusters performed by Xie Beni index of validity calculation at FCM method. Having obtained the optimum number of clusters, evaluation step was conducted by comparing FCM method to K-Means method with noticing the average of standard deviation in the clusters and the average of standard deviation inter-clusters (Sw/Sb) from each method. Method with the smallest Sw/Sb ratio value would get chosen as the best method. Based on the validity index Xie Beni, the most optimum number of cluster is 10 because it has the smallest Sw/Sb ratio value compared to FCM, the value is 0,6651. Afterwards, the result of K-Means clustering is analyzed to determined the interpretation and characteristics of each formed clusters.Keyword: Cluster Analysis, coupon rate, time to maturity, yield, rating, Fuzzy C-Means, K-Means, Xie Beni Index, Sw/Sb ratio.

ANALISIS KETAHANAN HIDUP PENDERITA TUBERKULOSIS DENGAN MENGGUNAKAN METODE REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus di Puskesmas Kecamatan Kembangan Jakarta Barat) Wulan Safitri; Triastuti Wuryandari; Suparti Suparti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tuberculosis (TB) is an infectious disease caused by the bacteria of the Mycobacterium groups that is Mycobacterium Tuberculosis. Most of the TB germs attack the lungs, but can also on other organs. In Indonesia based on the Survei Kesehatan Rumah Tangga (SKRT) in 2001 showed TB is the first cause of death in the group of infectious diseases. To determine the factors that affect the rate of healing of patients with TB is using regression analysis, because the dependent variable is the time of failure that equipped with censorship then used cox proportional hazard regression. Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is the phrase used to describe the analysis of data in the form of times from a well-defined time origin until the occurrence of some particular event or end-point. The cases examined in this study are the factors that affect the rate of healing of patients with TB in Puskesmas Kecamatan Kembangan Jakarta Barat. The conclusion state that the factors affecting the rate of healing of patients with TB are a source of transmitting and medicine records. Keywords: Tuberculosis, Survival Analysis, Cox Proportional Hazard Regression

ANALISIS INTEGRASI PASAR BAWANG MERAH MENGGUNAKAN METODE VECTOR ERROR CORRECTION MODEL (VECM) (Studi Kasus: Harga Bawang Merah di Provinsi Jawa Tengah) Rizky Aditya Akbar; Agus Rusgiyono; Tarno Tarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Spatial market integration is the degree of closeness of relationship between the regional market with other regional market. Vertical market integration is the level of the relationship between a marketing agency with other marketing agencies in the marketing. Spatial market integration in onion prices at the wholesale level for the area of Brebes, Tegal, Pemalang, Semarang, Salatiga, Surakarta and can be analyzed using the Vector Error Correction Model (VECM) to see where the long-term relationship. Vertical market integration in onion prices in the wholesale and consumer levels for Tegal, Semarang and Surakarta can be analyzed using the Granger Causality. The data used are the monthly time series data from January 2010 until February 2016, where data must be stationary at the first difference. Based on Johansen cointegration test obtained their long-term relationship at all six of the region and can be used to analyze VECM method. Granger Causality used as a test of causality. From this study, it can be concluded the onion market in Central Java is not fully integrated spatial whereby if shocks in Brebes then be transmitted to the market in Pemalang, Semarang, Salatiga and Surakarta whereas if shocks occur in Tegal will be transmitted to Semarang, Salatiga and Surakarta. This occurs because Brebes as the central region producing onion and Tegal as many areas in need of onion. The existence of vertical integration only occurs in Semarang, although only one-way causality. Keywords: Market Integration, Johansen Cointegration Test, VECM, Granger Causality Test.

OPTIMASI VALUE AT RISK RETURN ASET TUNGGAL DAN PORTOFOLIO MENGGUNAKAN SIMULASI MONTE CARLO DILENGKAPI GUI MATLAB Astuti, Nur Indah Yuli; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a scale that can measure the maximum loss that may happen for a specified period of time in the normal market conditions at a certain level of confidence. The most important thing in the VaR is to determine the type of methodology and assuming appropriate with the distribution of the return. One of the methods in calculating the VaR is Monte Carlo simulation. VaR with Monte Carlo simulation method assumes that the return value is normal distribution simulated using the appropriate parameters and portfolio return is linier towards its single asset return. From the results and analysis research conducted use GUI Matlab, VaR single asset of value risk on the stock of United Tractors Tbk (UNTR) is greater than Bank Rakyat Indonesia (Persero) Tbk (BBRI), Astra International Tbk (ASII), and Bank Negara Indonesia Tbk ( BBNI), VaR value of portfolio consisting of two assets, the three assets, and four assets have lower value than the sum of its single asset of the value of VaR. Matlab (Matrix Laboratory) is an interactive programming system with the basic elements of array database which dimensions do not need to be stated in particular, while the GUI is the submenu of Matlab. In this research, determining the level of trust and specified time period is very important to count of VaR value because it can describe how much investors bear the risk. Keywords: Value at Risk, time period, confidence level, Monte Carlo simulation

PEMODELAN MARKOV SWITCHING DENGAN TIME-VARYING TRANSITION PROBABILITY Anggita Puri Savitri; Budi Warsito; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate or currency is an economic variable which reflects country’s state of economy. It fluctuates over time because of its ability to switch the condition or regime caused by economic and political factors. The changes in the exchange rate are depreciation and appreciation. Therefore, it could be modeled using Markov Switching with Time-Varying Transition Probability which observe the conditional changes and use information variable. From this model, time-varying transition probability and expected durations are obtained; both are very useful to explain economic growth better and more detailed. This research modeled ln return value of Indonesian Rupiah to U.S Dollars and using ln return value of Indonesian Rupiah to Euro as information variable. The best model is MS(2) – AR(1). Overall, the mean of transition probability from appreciation to depreciation is 0,025242 and the transition probability from depreciation to appreciation is 0,666369. Expected duration of appreciation is 39,61623 days meanwhile the expected duration of depreciation is 39,18689 days. Keywords : regime switching, Markov switching, time-varying, transition probability, expected duration

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Jawa tengah

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