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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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Articles 733 Documents

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PENENTUAN MODEL SISTEM ANTREAN KENDARAAN DI GERBANG TOL BANYUMANIK SEMARANG Dedi Nugraha; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The arrival rate of vehicles that have occured at the Banyumanik tollgate is randomly and fluctuatly. Those condition would make difficult for tollgate management to determine policies in operating the substation service. If the substation service operates slightly, can occur long queues, especially at certain time. In the meantime, if the substation service operates many service, service to be inefficient. Therefore, it is necessary to determine the queuing system model in accordance with the conditions and characteristics of the queue from service facilities at the Banyumanik tollgate appropriately. So it can be determined the efektif and efisien number of service substation. Based on the analysis of data obtained, a queue model system that occurred at the Banyumanik tollgate is . The efektif number of substations service for directions Ungaran-Semarang are two subtations service. While for direction Semarang-Ungaran, the efektif number of substation service is three.

PEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DI PROVINSI JAWA TENGAH MENGGUNAKAN BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINES (BAGGING MARS) Maryam Jamilah An Hasibuan; Agus Rusgiyono; Diah Safitri
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Increased economic improvement is one way to improve people's welfare in certain areas. Gross Regional Domestic Product (GRDP) is one of the macroeconomic indicators used to measure economic growth in a region. Related to the economy in Central Java Province increased from year to year. Increasing economic growth is inseparable from the contribution of factors that sufficiently contribute to the GRDP. Factors that are the cause of GRDP are Regional Original Income, Foreign Investment, and Domestic Investment. The method used to model the factors that influence Gross Regional Domestic Product is the Multivariate Adaptive Regression Spline (MARS) method and combine it with Bagging. MARS method is one method that uses nonparametric regression and high dimension data. The best model used is a model with a combination of BF = 6, MI = 1, MO = 0 with GCV of 5.667,6680. Then bagging is done on the initial data set with 10, 25, 35, 40, 55, 75, 85, 90 and 100 bootstrap replications. GCV produced in bagging MARS 2.258,6192. GCV valuesobtained from MARS bagging are smaller compared to the MARS method. This shows that bagging can reduce the value of GCV and increase accuracy, making this method can be used in this study. Keywords: GRDP, GCV, MARS, Bagging

PENERAPAN FORMULA BENEISH M-SCORE DAN ANALISIS DISKRIMINAN LINIER UNTUK KLASIFIKASI PERUSAHAAN MANIPULATOR DAN NON-MANIPULATOR (Studi Kasus Di Bursa Efek Indonesia Tahun 2013) Issabella Marsasella Christy; Sugito Sugito; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant analysis is a statistical analysis method is used to classify an individual into a certain group which has determined based on the independent variables. In linear discriminant analysis, there are two assumptions to be fulfilled i.e. independent variables have to be multivariate normal distributed and variance covariance matrix of the observed two groups are the same. In this graduating paper is applied Beneish M-Score formula and linier discriminant analysis for classification of cases companies manipulators and non-manipulators are listed in Indonesia Stock Exchange in 2013. Linear discriminant function to continue Beneish M-Score formula to predict the classification, in order to obtain the percentage of fault classification, to determine the size of the performance of linear discriminant function. Percentage of classification error of 2,70 percent. Keywords: Beneish M-Score, Linear Discriminant Analysis

METODE REGRESI DATA PANEL UNTUK PERAMALAN KONSUMSI ENERGI DI INDONESIA Mariska Srihardianti; Mustafid Mustafid; Alan Prahutama
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Panel data regression is a method that aims to model the effect of one or more predictor variables on the response variable, observed in some sectors of an object of research for a specific time period. To estimate the panel data regression model, there are three approaches, namely Common Effect Model (CEM), Fixed Effects Model (FEM) and Random Effects Model (REM). In estimating the parameters for each model, there are several methods that can be used based on the assumption of the structure residual variance-covariance matrix, that is Ordinary Least Square/Least Square Dummy Variable (OLS/LSDV), Weighted Least Square (WLS) dan Seemingly Unrelated Regression (SUR). This research aims to implement the panel data regression to analyze the effect of GDP on energy consumption in Indonesia for each sector. Panel data regression model that has been obtained then is used to predict the amount of energy consumption in Indonesia for each sector in 2015 and 2016 using trend analysis. The analysis showed that the panel data regression model corresponding to the data of energy consumption in Indonesia in 1990-2014 is Fixed Effect Model (FEM) with Cross-section SUR, with R2 value is 0.975943. Forecasting results show energy consumption in Indonesia in 2015 and 2016 will increase to the household sector and transport. Whereas for industrial, commercial and others sectors will decline in 2015 and then increase in 2016. Keywords : Panel Data, Fixed Effect Model, SUR, Trend Analysis, Energy Consumption

REGRESI ROBUST MM-ESTIMATOR UNTUK PENANGANAN PENCILAN PADA REGRESI LINIER BERGANDA Sherly Candraningtyas; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The multiple linear regression model is used to study the relationship between a dependent variable and more than one independent variables. Estimation method which is the most frequently be used to analyze regression is Ordinary Least Squares (OLS). OLS for linear regression models is known to be very sensitive to outliers. Robust regression is an important method for analyzing data contaminated by outliers. This paper will discuss the robust regression MM-estimator. This estimation is a combined estimation method which has a high breakdown value (LTS-estimator or S-estimator) and M-estimator. Generally, there are three steps for MM-estimator: estimation of regression parameters initial using LTS-estimators, residual and robust scale using M-estimator, and the final estimation parameter using M-estimator. The purpose of writing this paper are to detect outliers using DFFITS and determine the multiple linear regression equations containing outliers using robust regression MM-estimator. The data used is the generated data from software Minitab 14.0. Based on the analysis results can be concluded that data 21st, 27th, 34th are outliers and equation of multiple linear regression using robust regression MM-estimators is .

COPULA FRANK PADA VALUE at RISK (VaR) PEMBENTUKAN PORTOFOLIO BIVARIAT (Studi Kasus : Saham-Saham Perusahaan yang Meraih Predikat The IDX Top Ten Blue Tahun 2017 dengan Periode Saham 20 Oktober 2014 – 28 Februari 2018) Juria Ayu Handini; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market has an important role in society to invest in financial instruments. Investors can invest in the form of a portfolio that is by combining several shares to reduce the risk that will occur. Value at Risk (VaR) is a method for estimating the worst risk of an investment. GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is used to model high-volatile stock data that causes residual variance is not constant. Copula theory is a powerful tool for modeling joint distributions because it does not require normality assumptions that are difficult to fulfill in financial data. Copula Frank has a feature that can identify positive and negative dependencies. This study aims to measure the value of VaR using the Frank-GARCH copula method using stock returns data of PT Bank Rakyat Indonesia, Tbk (BBRI), PT Telekomunikasi Indonesia, Tbk (TLKM), and PT. Unilever Indonesia, Tbk (UNVR) for the period 20 October 2014 - 28 February. Bivariate portfolio pairs obtained namely TLKM and UNVR shares because they have the highest Rho Spearman residual correlation value of ρ = 0.3204. Based on the generation of data using Monte Carlo simulations, the results of the calculation of Value at Risk (VaR) of 1.40% at the 90% confidence level, 1.89% at the 95% confidence level, and 2.79% at the 99% confidence level. Keywords: Value at Risk, Frank copula, GARCH, Monte Carlo

PEMODELAN DATA INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI PENALIZED SPLINE Novia Agustina; Suparti Suparti; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia Composite Index (IHSG) is an indicator of stock price changes in Indonesia Stock Exchange. IHSG is time series data that can be modeled with parametric models. But there are some assumptions for parametric model, while the fluctuated IHSG data usually doesn’t occupy these assumptions. Another alternative for this study is nonparametric regression. Penalized spline regression is one of nonparametric regression method that can be used. The optimal penalized spline models depends on the determination of the optimal smoothing parameter λ and the optimal number of knots, that has a minimum value of Generalized Cross Validation (GCV). The best model in this study is penalized spline degree 1 (linear) with 1 knot, that is 5120,625, smoothing parameter λ value is 41590, and GCV value is 1567,203. R2 value for in sample data is 83,2694% and R2 value for out sample data is 96,4976% show that the model have a very good performance. MAPE values for in sample data is 0,5983% and MAPE values for out sample data is 0,4974%. Because the value of MAPE in sample and out sample is less than 10%, it means that the performance of the model and forecasting are very accurate. Keywords: Indonesia Composite Index, Nonparametric Regression, Penalized Spline Regression, GCV, MAPE

MODEL REGRESI COX PROPORTIONAL HAZARDS PADA DATA LAMA STUDI MAHASISWA (Studi Kasus Di Fakultas Sains dan Matematika Universitas Diponegoro Semarang Mahasiswa Angkatan 2009) Landong Panahatan Hutahaean; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

High education has important role to increase the intellectual life of the nation and the development of natural sciences and technology by producing the quality graduates. The quality graduates just need 48 month to finish the study. There are many factors that will affect time of study students as Grade Point Average(GPA), Bustle student level, etc. Hence, need to know what factors affecting time of study students. One method that can be used is Survival analysis. Survival Analysis is analysis of survival data from the beginning of time research until certain events occurred. One of the methods of survival analysis is Cox Proportional Hazards Regression. Cox Proportional Hazards Regression is a regression which used data of intervals of time an event. The case which is discussed in this research is factors that affect time of study students of Faculty of Science and Mathematics started 2009 Diponegoro of University with the second type of censoring. From the research give conclusion that factors affecting time of study students is Department, GPA, and Organization

IMPLEMENTASI METODE SAW DAN WASPAS DENGAN PEMBOBOTAN ROC DALAM SELEKSI PENERIMAAN PESERTA DIDIK BARU (Studi Kasus: Madrasah Tsanawiyah (MTs) Negeri Kisaran Kabupaten Asahan Provinsi Sumatera Utara Tahun Ajaran 2018/2019) Nabila, Eva Salsa; Rahmawati, Rita; Widiharih, Tatik
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multi Attribute Decision Making (MADM) is one of the decision-making methods to determine the best alternative from a number of alternatives based on certain criteria. There are several methods that can be used to solve MADM problems including Simple Additive Weighting (SAW) and Weighted Aggregated Sum Product Assesmen (WASPAS). Both methods are applied in the selection of prospective new students. In this study, MTsN Kisaran selected 192 students received from 422 registrans and determined certain criteria to get quality students. The criteria determined are the value of the national exam, the value of the Al-Qur'an test, and the value of the academic potential test. The method applied is SAW and WASPAS with theÂ weighting Rank Order Centroid (ROC). Then a sensitivity analysis is carried out to determine a viable methods selected to obtain optimal results. This research was designed with the help of the Matlab GUI as a computing tool to simplify and accelerate the selection process. Based on the results of the study, the average percentage value of sensitivity for the SAW method was -0.82% while the WASPAS method was -0.87%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the SAW method.Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Keywords: Students, SAW, WASPAS, ROC, Sensitivity, GUI Matlab.

ANALISIS ANTRIAN PENGUNJUNG DAN KINERJA SISTEM DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KOTA SEMARANG Astrelita, Fahra Pracendi; Sugito, Sugito; Wuryandari, Triastuti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Department of Population and Civil Registration (Dispendukcapil) has the duty of assistance in the field of population and civil registration. Civil registration services such as services related to birth, death, marriage, and divorce. As a service provider, Dispendukcapil of Semarang has the motto "No Day Without Service Quality Improvement". Queuing problem is that often occur and must be considered. The queue situation occurs because the number of visitors to a service facility exceeds the available capacity to perform such services. A system is always trying to serve visitors well in accordance with the rate of arrival of each visitor. Therefore please note the size of the system's performance on each section on service system. Dispendukcapil queuing system at Semarang city located on the Legalized, Change Data, Birth, Death, Divorce/Marriage, and Decision Act. Based on the results obtained and the analysis of models of queuing at the counter is Legalized (G/G/2):(GD/∞/∞), while the counter is Birth (G/G/3):(GD/∞/∞), the Change the counter Data, Death, Divorce / Marriage is (M/G/1):(GD/∞/∞) and Decision Deed is (G/G/1):(GD/∞/∞). Keywords: Queuing System, Dispendukcapil, Dispendukcapil of Semarang, Legalized, Birth, Death, Divorce, Marriage.

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