Agus Rusgiyono
Universitas Diponegoro

Published : 82 Documents
Articles

ANALISIS FAKTOR-FAKTOR PRODUKSI PERIKANAN TANGKAP PERAIRAN UMUM DARATAN DI JAWA TENGAH MENGGUNAKAN REGRESI BERGANDA DAN MODEL DURBIN SPASIAL Retnowati, Puji; Rahmawati, Rita; Rusgiyono, Agus
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Indonesia?s inland openwater is the second largest in Asia after China. It?s estimated  Indonesia?s inland openwater capture fisheries potential reached 3.034.934 tons per year. Central Java is one of the provinces that have great potential in the field of fisheries. In this study will be discussed about the factors suspected to affect inland openwater capture fisheries production. The method used are multiple regression analysis with maximum likelihood estimation and spatial durbin models. Spatial durbin models is the development of linear regression which location factors are also considered. The results of spatial dependences shows there is spatial dependence in the inland openwater capture fisheries production variable, fisheries establishments variables and the number of boats variable. So spatial durbin models can be used for analysis. In spatial durbin models, variables that significantly influence inland openwater capture fisheries production is the number of fishing gear, the number of boats, and the number of fishing trip with coefficient of determination (R2) of 0,9054. While in the multiple regression analysis showed that the only number of fishing trip variable that significantly, where the value of the coefficient of determination (R2) is 0,857. Thus better spatial durbin models used to analyze inland openwater capture fisheries production, in addition more significant variables also have the coefficient of determination (R2) that is greater than the multiple regression analysis.Keywords: inland openwater capture fisheries production, maximum likelihood, spatial durbin model.
PERBANDINGAN METODE VARIANCE COVARIANCE DAN HISTORICAL SIMULATION UNTUK MENGUKUR RISIKO INVESTASI REKSA DANA Wicaksono, Bayu Heryadi; Wilandari, Yuciana; Rusgiyono, Agus
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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One of the instruments of financial assets are investments in mutual funds. Every day of the total fair value of the assets in the mutual fund is always changing because the market value of each type of asset that is changing. Thus causing mutual fund has a risk. It is necessary for the measurement of risk in mutual funds using the Value at Risk (VaR). There are three methods of calculating the VaR Variance-covariance method, Monte Carlo simulation methods and methods Historical Simulation. In this study, the variance-covariance method used and the Historical Simulation method to measure potential losses on investments largest mutual fund shares at 95% confidence level. The test used is the Kolmogorov-Smirnov normality test and Kupiec test return data to test the accuracy of the calculation of VaR. Because the data are not normally distributed returns, the adjustment is then performed using the Cornish-Fisher Expansion. By using the t test results show that the calculation of VaR with variance-covariance and Historical Simulation did not differ significantly. The test results show that the accuracy of the VaR VaR accurately all used to measure the magnitude of the maximum potential loss on investments in mutual fund shares. Keywords : Value at Risk (VaR), Variance-covariance, Historical Simulation, Mutual Fund, Risk.
PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAN TAHUN 2008-2013 DENGAN MENGGUNAKAN REGRESI DATA PANEL Rizki, Muhammad; Rusgiyono, Agus; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Human Development Index (HDI) is a way to measure the success of human development based on a number of basic components quality of life. HDI is formed by three basic variables namely health, education and decent living standards. This study aims to identify factors that influence the Human Development Index in Central Java Province and get a model Human Development Index in Central Java province in 2008-2013. The data used in this study is a combination of cross section data and time series data are commonly called panel data, then this HDI modeling using panel data regression. There are three estimation of panel data regression model namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM).  Estimation of panel data regression model used is the Fixed Effects Model (FEM). FEM estimation results show the number of health facilities, school participation rate and Labor Force Participation Rate significantly affect the HDI by generating  for 93.58%.Keywords : Fixed Effect Model, panel data regression, HDI in Central Java Province
PEMODELAN JUMLAH UANG BEREDAR MENGGUNAKAN PARTIAL LEAST SQUARES REGRESSION (PLSR) DENGAN ALGORITMA NIPALS (NONLINEAR ITERATIVE PARTIAL LEAST SQUARES) Ikadianti, Riana; Rahmawati, Rita; Rusgiyono, Agus
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Money supply has a tendency to increase or decrease the price level. Because of it, it is important to do a restraint and control action on money supply through its affecting factors include net foreign assets, net claims on central government, claims on region government, claims on the other finances institution, claims on nonfinances enterprise of state-owned corporation, and claims on private sector. In this study, a model has done between money supply and its affecting factors using Partial Least Squares Regression (PLSR) with NIPALS (Nonlinear Iterative Partial Least Squares) algorithm because the affecting factors of money supply data is detected multicollinearity. In the PLSR, regression coefficient is obtained iteratively. Three stage iteration process in PLSR produce weight vector, loading vector, and parameter estimation that produce PRESS and R2 values later. Based on the analysis, PLSR model to the money supply data in July 2012 until December 2014 is obtained at the fourth iteration with minimum PRESS value as 2,10815x1010. That PLSR model has R2 value as 99,47%, so it is very good for explaining the money supply. By means of bootstrap technique, concluded that all of the affecting factors of money supply on PLSR model influence money supply significantly. Keywords: money supply, multicollinearity, PLSR, NIPALS
ANALISIS FAKTOR-FAKTOR TINGKAT KEMISKINAN DI KABUPATEN WONOSOBO DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION Permana, Maulana Taufan; Yasin, Hasbi; Rusgiyono, Agus
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
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Poverty reduction is the main priority in development strategies in Indonesia, but during this computation is modeled as a function of the poor global regression. That is, the value of the regression coefficient applies to all geographic regions. In reality each region has different characteristics according to the geographical location, therefore Geographically Weighted Regression models are developed (GWR). GWR model is used to consider the element of geography or location as the weighting in estimating the model parameters. In the model GWR model parameter estimation is obtained by using Weighted Least Square (WLS) is to give a different weighting at each location. This study discusses the factors that affect the level of poverty in the District Wonosobo. The results of testing the suitability of the model shows that there is no spatial factors influence the level of poverty in the District Wonosobo. Based on research, there are 3 variables thought to affect the level of household poverty in Wonosobo district, percentage of the number of families that have slums, percentage number of families severely malnourished, percentage of the number of families who have agricultural land. These variables have a similar effect in each district.Keywords: Poverty, Geographically Weighted Regression, Weighted Least Square, Wonosobo
PENENTUAN FAKTOR-FAKTOR YANG MEMPENGARUHI INTENSITAS CURAH HUJAN DENGAN ANALISIS DISKRIMINAN GANDA DAN REGRESI LOGISTIK MULTINOMIAL (STUDI KASUS: DATA CURAH HUJAN KOTA SEMARANG DARI STASIUN METEOROLOGI MARITIM TANJUNG EMAS PERIODE OKTOBER 2018 – MARET 2019) Rohmana, Shella Faiz; Rusgiyono, Agus; Sugito, Sugito
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the research of grid data and the method of grouping rainfall. According to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencing rainfall grouping using multiple discriminant analysis with a stepwise selection method. This study uses the daily climate data of Semarang City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall grouping are air temperature, air humidity, and wind direction. This analysis produces discriminant scores obtained from linear combinations between discriminant weights and observation values of significant independent variable. The classification procedure is based on the discriminant score each observations compared to cutting score resulted in classification accuracy of 62.89%. Multinomial logistic regression analysis is used to determine the effect of independent variables on rainfall intensity using the odds ratio. This analysis produces an estimate of the conditional probability of each group using significant independent variables are air temperature, air humidity, wind speed, and wind direction. The classification procedure is based on the largest conditional probability value between rainfall groups resulted in classification accuracy of 69.80%. Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall
PERBANDINGAN MODEL ARIMA DAN FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN KABUPATEN WONOSOBO Hidayah, Siti Lis Ina Atul; Rusgiyono, Agus; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
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Rainfall is one of the things that affect agricultural production. The highest amount of rainfall will cause perturbation in the pollination of flowers and caused zalacca palm to produce fruits no season of the year. Zalacca palm is growing well in heavy rainfall area.. There are some factors which influence rainfall; those are: humidity, solar energy, wind direction and velocity as well as air temperature.  The application of ARIMA (Autoregressive Integrated Moving Average) and multi input transfer function was intended to model the rainfall which would be forecasted based on the best model chosen. There were two kinds of variables used in this study. Those were rainfall as the output series while humidity and air temperature as the input series during January 2009 to October 2014. The result showed that ARIMA ([3], 1, [12]) had a fewer Schwart?z Bayesian Criterion (SBC) value 293.199 than multi input transfer function model (0,0,0) (0,1,0) with the result 906.9632.Keywords: Rainfall, ARIMA, Transfer Function
ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT Maharunti, Ungu Siwi; Mukid, Moch. Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
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Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank ?X? in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk.  The classification using the Fisher multiple discriminant analysis in data of credit client from bank ?X? in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis
PERHITUNGAN DAN ANALISIS PRODUK DOMESTIK REGIONAL BRUTO (PDRB) KABUPATEN/KOTA BERDASARKAN HARGA KONSTAN (STUDI KASUS BPS KABUPATEN KENDAL) Fitriani, Fitriani; Rusgiyono, Agus; Wuryandari, Triastuti
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
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Gross Regional Domestic Product (GRDP) is technical term that always we heard in the civil government or in the public society. According to Statistics Indonesia, GRDP is total number of added value who producting by effort unit in that domestic area. GRDP is one of economics growth indicator in the domestic area. If GRDP is higher, then people economics prosperity must be high too, and do also that opposite. GRDP contains of 2 methods, that is GRDP at Current Market Prices and GRDP at Constant Prices. In this report will discuss about GRDP at Constant Prices with GRDP the Kendal Regency at 2000 Constant Prices in 2010 for example. Arranging GRDP at Constant Prices has purpose to find out economics condition from year to year by discern the GRDP every year. The methods to arranging GRDP at Constant Prices are revaluasi, ekstrapolasi, and deflasi. After doing the accounting by Statistics Indonesia, we obtainable GRDP the Kendal Regency at Constant Prices in 2010 in million rupiahs is 5.394.079,31. And according the analysis, GRDP from 1983 to 2011 show the linear graph that has model GRDP = -986933 +  220901 (X). This model, can use to forecasting for GRDP the Kendal Regency at Constant Prices over the next years.
ANALISIS KOMODITAS UNGGULAN PERIKANAN BUDIDAYA PROVINSI JAWA TENGAH TAHUN 2012-2016 MENGGUNAKAN METODE LOCATION QUOTIENT DAN SHIFT SHARE Manullang, Dian Mariana L; Rusgiyono, Agus; Warsito, Budi
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Condition of capture fisheries is currently stagnating, even tended to decline, which is indicated by the decrease of production in some fishery development areas in Indonesia. Aquaculture is one solution that can be done. Central Java Province is a province that has a large aquaculture potential, therefore of course Central Java province has leading commodities that become the sector of regional economic development. This research discusses about the potential location for the development of each leading commodities in Central Java Province as a recommendation related to the centre of fisheries production. Analytical methods in this research are Location Quotient (LQ) dan Shift share. It used to see how big these locations have a potential in the development of aquaculture production and to identify spatial autocorrelation in the amount of aquaculture production using Moran?s index. The analysis of LQ and shift share shows that each district has a different potential in the development of leading commodities production. The value of the Moran?s index obtained equal to -0.1381, that is in the range of -1 <I ? 0, indicating that the presence of spatial autocorrelation is negative but small because of near to zero. It can be concluded that there is no similarity of the values between the districts or indicate that amount of aquaculture production among the districts in Central Java are not correlated.Keywords: Leading Commodities, Location Quotient (LQ), Shift Share, Moran?s  Index
Co-Authors Abdul Hoyi Abdul Hoyyi Agustina Sunarwatiningsih Akbar, Rizky Aditya Alan Prahutama Anggita, Esta Dewi Anifa Anifa Annisa Rahmawati Arief Rachman Hakim Arif, Besya Salsabilla Azani Aulia Putri Andana, Aulia Putri Bayu Heryadi Wicaksono Bellina Ayu Rinni Bramaditya Swarasmaradhana Budi Warsito Dede Zumrohtuliyosi Dermawanti Dermawanti, Dermawanti Di Asih I Maruddani Diah Safitri Diarsih, Inas Husna Dini Anggreani Dwi Ispriyanti Enggar Nur Sasongko, Enggar Nur Etik Setyowati, Etik fatimah Fatimah Feby Kurniawati Heru Prabowo, Feby Kurniawati Heru Fitriani Fitriani Hana Hayati Hanik Rosyidah, Hanik Hardi, Desy Tresnowati Hasbi Yasin Hasibuan, Maryam Jamilah An Hildawati Hildawati, Hildawati Ilham Muhammad Ispriyansti, Dwi Iwan Ali Sofwan Listifadah Listifadah M. Afif Amirillah M. Atma Adhyaksa Manullang, Dian Mariana L Maulana Taufan Permana Merlia Yustiti Moch. Abdul Mukid Mooy, Marthin Nosry Muhammad Rizki Muhammad Taufan Mustafid Mustafid Mustofa, Achmad Nababan, Tri Yani Elisabeth Nisa, Aulia Rahmatun Noveda Mulya Wibowo, Noveda Mulya Novie Eriska Aritonang, Novie Eriska Nurhayati, Ika Chandra Octafinnanda Ummu Fairuzdhiya Rakhmawati, Dwi Asti Rengganis Purwakinanti Retnowati, Puji Revaldo Mario, Revaldo Riana Ikadianti, Riana Rifkhatussa'diyah, Ely Fitria Rita Rahmawati Rizal Yunianto Ghofar Rohmana, Shella Faiz Rosita Wahyuningtyas Rukun Santoso Setiyowati, Eka Siti Lis Ina Atul Hidayah, Siti Lis Ina Atul Sudargo Sudargo, Sudargo Sudarno Sudarno Sugito Sugito Suparti Suparti Susi Ekawati sutimin sutimin Tarno Tarno Tiani Wahyu Utami Tika Dhiyani Mirawati Tika Nur Resa Utami, Tika Nur Resa Titis Nur Utami, Titis Nur Triastuti Wuryandari Tyas Ayu Prasanti, Tyas Ayu Tyas Estiningrum Ungu Siwi Maharunti, Ungu Siwi Vierga Dea Margaretha Sinaga, Vierga Dea Margaretha Viliyan Indaka Ardhi, Viliyan Indaka Walidaini, Nur Winastiti, Lugas Putranti Yuciana Wilandari