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All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Agus Rusgiyono

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 366013

Decision Sciences, Operations Research & Management Mathematics Other

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KLASIFIKASI STATUS KERJA PADA ANGKATAN KERJA KOTA SEMARANG TAHUN 2014 MENGGUNAKAN METODE CHAID DAN CART Novie Eriska Aritonang; Agus Rusgiyono; Rita Rahmawati
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The growth of labor will increase along with increasing population. Increasing the number of this labor of course going to have an impact on his status, whether employ or unemployed. The method can be used to classify the status of the labor is CHAID (Chi-squared Automatic Interaction Detection) and CART (Classification and Regression Trees). Both of these methods aim to identify factors that influence employment status. These methods will be applied for Semarang labor data in 2014. Based on CHAID method, the factors that affect the status of the labor is gender, age and status of the completeness of a life partner with accuracy classification results amounted to 72.63%. Factors that affect the status of the labor force with the CART method is gender, age, educational status, and the status of the completeness of a life partner with the accuracy of the classification is 72.79%. Based on proportion test, these methods are same of doing classification employment status.Keywords: Labor, Classification, CHAID, CART, Accuracy of classification

PENERAPAN RESPONSE BASED UNIT SEGMENTATION IN PARTIAL LEAST SQUARE (REBUS-PLS) UNTUK ANALISIS DAN PENGELOMPOKAN WILAYAH (Studi Kasus: Kesehatan Lingkungan Perumahan di Provinsi Jawa Tengah) Febriana Sulistya Pratiwi; Sudarno Sudarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28927

Residental environmental health is a complex problem that depends on several dimensions. One of the statistical method that can be used to analyze the relation between complex dimensions is Structural Equation Modeling (SEM) with a variant/component based approach or Partial Least Square. The purpose of this study is to develop a structural model of the relation between household economy, education, housing facilities, and residental environmental health in Central Java Province in 2018 based on 12 valid and reliable indicators. In the structural equation model there is a significant positive effect path that is the influence of household economy towards education and towards housing facilities, and influence housing facility on the residential environment health. In SEM analysis it is generally assumed that the data taken comes from a homogeneous population but often the data consists of several segments. Therefore, we need a method to detect heterogeneity problems, namely Response Based Unit Segmentation in Partial Least Square (REBUS-PLS). Based on the dendogram produced, by forming 2 classes/segments, values as the accuracy of the prediction model on the local model had a higher value (except values for Education in local model 2) than values on the global model. In addition, the Goodnes of Fit value as a measure of model suitability for each local model is also had a higher value, so that it indicates the goodness of the model in the local model is better than the global model.Keywords: environmental health, SEM, PLS, REBUS-PLS

KLASIFIKASI KELULUSAN MAHASISWA FAKULTAS SAINS DAN MATEMATIKA UNIVERSITAS DIPONEGORO MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) Rizal Yunianto Ghofar; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Education is a top priority for today's society. The quality of education can be seen from the learning achievement. There are so many factors that influence learning achievement in this regard graduation, therefore, necessary to identify the most influential factors that will be used to improve the quality of education. This study was conducted to obtain a model that is capable of classifying the data Faculty of Science and Mathematics Diponegoro University Semarang graduation using Multivariate Adaptive Regression Spline (MARS) method. MARS is a nonparametric regression method that can be used for data of high dimension. To get the best MARS models, made possible combinations Basis Function (BF), Maximum Interaction (MI), and Minimum Observation (MO) by trial and error. The best model is the model that is used in combination with BF = 28, MI = 2, MO = 1 because it has the smallest GCV value that is equal to 0,17781. There are three variables that contribute to the MARS model of the variable GPA, majors and gender. As for the variable organization, part time, entry point, and scholarships do not contribute to the model. Obtained misclassification of 20,50%. Press's Q test value indicates that statistically MARS method has been consistent in classifying the data FSM Diponegoro University Semarang graduation.

PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS Tri Yani Elisabeth Nababan; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.

PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Inas Husna Diarsih; Tarno Tarno; Agus Rusgiyono
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS

ANALISIS FAKTOR-FAKTOR PRODUKSI PERIKANAN TANGKAP PERAIRAN UMUM DARATAN DI JAWA TENGAH MENGGUNAKAN REGRESI BERGANDA DAN MODEL DURBIN SPASIAL Puji Retnowati; Rita Rahmawati; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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.

PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAN TAHUN 2008-2013 DENGAN MENGGUNAKAN REGRESI DATA PANEL Muhammad Rizki; Agus Rusgiyono; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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

PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA) Desy Tresnowati Hardi; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

PEMODELAN JUMLAH UANG BEREDAR MENGGUNAKAN PARTIAL LEAST SQUARES REGRESSION (PLSR) DENGAN ALGORITMA NIPALS (NONLINEAR ITERATIVE PARTIAL LEAST SQUARES) Riana Ikadianti; Rita Rahmawati; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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

PERBANDINGAN METODE VARIANCE COVARIANCE DAN HISTORICAL SIMULATION UNTUK MENGUKUR RISIKO INVESTASI REKSA DANA Bayu Heryadi Wicaksono; Yuciana Wilandari; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

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.

Co-Authors Abdul Hoyi Abdul Hoyyi Agustina Sunarwatiningsih Alan Prahutama Alan Prahutama Andreanto Andreanto Anggita, Esta Dewi Anifa Anifa Anindita Nur Safira ANNISA RAHMAWATI Annisa Rahmawati Arief Rachman Hakim Aulia Putri Andana Aulia Rahmatun Nisa Bagus Arya Saputra Bayu Heryadi Wicaksono Bellina Ayu Rinni Besya Salsabilla Azani Arif Bramaditya Swarasmaradhana Budi Warsito Dede Zumrohtuliyosi Dermawanti Dermawanti Desy Tresnowati Hardi Di Asih I Maruddani Diah Safitri Diah Safitri Dian Mariana L Manullang Dini Anggreani Diyah Rahayu Ningsih Dwi Asti Rakhmawati Dwi Ispriyansti Dwi Ispriyanti Eis Kartika Dewi Ely Fitria Rifkhatussa'diyah Elyasa, Fatiya Rahmita Enggar Nur Sasongko Etik Setyowati Etik Setyowati, Etik Farisiyah Fitriani fatimah Fatimah Febriana Sulistya Pratiwi Feby Kurniawati Heru Prabowo Fitriani Fitriani Hana Hayati Hanik Malikhatin Hanik Rosyidah, Hanik Hasbi Yasin Hasbi Yasin Hildawati Hildawati Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Ilham Muhammad Imam Desla Siena Inas Husna Diarsih Iwan Ali Sofwan Kevin Togos Parningotan Marpaung Listifadah Listifadah M. Afif Amirillah M. Atma Adhyaksa Marthin Nosry Mooy Maryam Jamilah An Hasibuan Maulana Taufan Permana Merlia Yustiti Moch. Abdul Mukid Moch. Abdul Mukid Muhammad Rizki Muhammad Taufan Mustafid Mustafid Mustafid Mustafid Mustofa, Achmad Nabila Chairunnisa Nor Hamidah Noveda Mulya Wibowo Novie Eriska Aritonang Nur Khofifah Nur Walidaini Octafinnanda Ummu Fairuzdhiya Puji Retnowati Puspita Kartikasari Putri Fajar Utami Rengganis Purwakinanti Revaldo Mario Ria Sulistyo Yuliani Riana Ikadianti Riszki Bella Primasari Rita Rahmawati Rita Rahmawati Rizal Yunianto Ghofar Rizky Aditya Akbar Rosita Wahyuningtyas Rukun Santoso Salsabila Rizkia Gusman Setiyowati, Eka Shella Faiz Rohmana Siti Lis Ina Atul Hidayah Sudargo Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suparti Suparti Suparti Suparti Susi Ekawati sutimin sutimin Tarno Tarno Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tiani Wahyu Utami Tika Dhiyani Mirawati Tika Nur Resa Utami, Tika Nur Resa Titis Nur Utami Tri Ernayanti Tri Yani Elisabeth Nababan Triastuti Wuryandari Triastuti Wuryandari Tyas Ayu Prasanti Tyas Estiningrum Ulfi Nur Alifah Ungu Siwi Maharunti Uswatun Hasanah Vierga Dea Margaretha Sinaga Viliyan Indaka Ardhi Winastiti, Lugas Putranti Yogi Isna Hartanto Yuciana Wilandari Yuciana Wilandari Yuciana Wilandari

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