Rukun Santoso
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KAJIAN PEMODELAN SPLINE UNTUK DATA LONGITUDINAL SEBAGAI PERKEMBANGAN DARI REGRESI NONPARAMETRIK Suparti, Suparti; Prahutama, Alan; Santoso, Rukun
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Regression analysis can be approached by using parametric, semi-parametricand nonparametric regression approaches. One of nonparametric regressionapproach that great developed was Spline truncated, including for modelinglongitudinal data. Longitudinal data is data that consisting of several subjectswhich is each subject is observed repeatedly based on a certain time. Theadvantages of longitudinal data has provided more complexcityof  informationthan cross section and time series data. The spline approach was a segmentedpolynomial regression approach. Spline provides high flexibility due to the useof knot points. To determine the optimal knot points using Generalized CrossValidation (GCV). The principle of determining the optimum point of knot oflongitudinal data using spline truncated is basically the same as with Splinemethod  for cross section data, that is determination of knot point based on eachsubject. However, the estimation is done simultaneously so that each subject hasits own model. Keywords: Spline Truncated, GCV, Knot points.
KOMPUTASI METODE SAW DAN TOPSIS MENGGUNAKAN GUI MATLAB UNTUK PEMILIHAN JENIS OBJEK WISATA TERBAIK (STUDI KASUS : PESONA WISATA JAWA TENGAH) Sari, Rima Nurlita; Santoso, Rukun; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Multi-Attribute Decision Making (MADM) is a method of decision-making to establish the best alternative from a number of alternatives based on certain criteria. Some of the methods that can be used to solve MADM problems are Simple Additive Weighting (SAW) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). SAW works by finding the sum of the weighted performance rating for each alternative in all criteria. While TOPSIS uses the principle that the alternative selected must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Both of these methods were applied in making the selection of the best tourist attractions in Central Java. There are 15 tourist attractions and 7 criteria: location, infrastructure, beauty, atmosphere, tourist interest, promotion, and cost. This primary research employed a questionnaire that passed the questionnaire testing, namely its validity and reliability test. The result of this study shows that the best type of tourism according to the government is temple tour. While water sports tourism is favored by tourism observers. As for college students, the preferred tourist destination is religious tourism. This study also produced a GUI Matlab programming application that can help users in performing data processing using SAW and TOPSIS to select the best attraction in Central Java. Keywords: MADM, SAW, TOPSIS, GUI, tourism
KLASIFIKASI CALON PENDONOR DARAH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER (STUDI KASUS : CALON PENDONOR DARAH DI KOTA SEMARANG) Bayususetyo, Dhimas; Santoso, Rukun; Tarno, Tarno
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. There are some methods that are included in the classification methods, one of them is Naïve Bayes. Naïve Bayes is a prediction technique that based simple probabilistic are based on the application of Bayes theorem with strong independence assumption. On this study carried out correction to the Naïve Bayes method in calculating the conditional probability of each feature using two approaches,  normal density function and cumulative distribution function approaches. These two approaches are used to classify prospective blood donors in Semarang City. The predictor variables used are hemoglobin level, upper blood pressure, lower blood pressure, and weight. The result of this study shows that both approaches have the same Matthews Correlation Coefficient (MCC) values, 0.8985841 or close to +1. It means that both approaches equally well doing classification.Keywords: Classification, Naïve Bayes, Normal Density Function, Cumulative Distribution Function, Blood Donors, Matthews Correlation Coefficient (MCC).
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) MENGGUNAKAN ANALISIS REGRESI LINIER DENGAN BOOTSTRAP (STUDI KASUS: PT. UNILEVER INDONESIA, TBK TAHUN 1999-2015) Safitri, Lia; Maruddani, Di Asih I; Santoso, Rukun
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

The amount of dividend paid by the company to shareholders or dividend payout ratio is the main factor that investors pay attention to invest their capital into the company. Investors want a relative dividend, even increasing over time. Factors influencing the level of dividend payout ratio are Return on Equity (ROE), stock price, liquidity ratio, and leverage level. Based on this, multiple linear regression analysis with bootstrap is used. The purpose of this study is to analyze the factors that significantly affect the dividend payout ratio based on the best model used to predict the value of dividend payout ratio for the next period. The bootstrap method is used to overcome the occurrence of multicollinearity among independent variables due to the small sample size. Based on the simulation done with software R using PT data. Unilever Indonesia, Tbk from 1999-2015 obtained best model is bootstrap residual with 2 significant independent variable are ROE and level of leverage. Based on the best model, the predicted value of dividend payout ratio of 2016 is 41.60196 with percentage error of 7.0812%. Keywords : Regression analysis, Bootstrap, Dividend Payout Ratio, ROE, leverage 
PEMODELAN DEFORESTASI HUTAN LINDUNG DI INDONESIA MENGGUNAKAN MODEL GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION (GTWR) Adiningrumh, Thea Zulfa; Prahutama, Alan; Santoso, Rukun
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Regression analysis is a statistical analysis method that is used to modeling the relationship between dependent variables and independent variables. In the linear regression model only produced parameter estimators are globally, so it?s often called global regression. While to analyze spatial data can be used Geographically Weighted Regression (GWR) method. Geographically and Temporally Weighted Regression (GTWR) is the development of  GWR model to handle the instability of a data both from the spatial and temporal sides simultaneously. In this GWR modeling the weight function used is a Gaussian  Kernel, which requires the bandwidth value as a distance parameter. Optimum bandwidth can be obtained by minimizing the CV (cross validation) coefficient value. By comparing the R-square, Mean Square Error (MSE) and Akaike Information Criterion (AIC) values in both methods, it is known that modeling the level of deforestation in protected forest areas in Indonesia in 2013 through 2016 uses the GTWR method better than global regression. With the R-square value the GTWR model is 25.1%, the MSE value is 0.7833 and AIC value is 349,6917. While the global regression model has R-square value of 15.8%, MSE value of 0.861 and AIC value of 361,3328. Keywords : GWR, GTWR, Bandwidth, Kernel Gaussian
PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (STUDI KASUS: SAHAM PERUSAHAAN THE IDX TOP TEN BLUE 2017) Situmorang, Ria Epelina; Maruddani, Di Asih I; Santoso, Rukun
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).
TERAPAN FUNGSI DENSITAS EMPIRIK DENGAN PENDEKATAN DERET FOURIER UNTUK ESTIMASI DIAGRAM PENGENDALI KUALITAS Santoso, Rukun
MATEMATIKA Vol 10, No 3 (2007): JURNAL MATEMATIKA
Publisher : MATEMATIKA

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Abstract

Any continues function on the Hilbert space L2[-p,p] can be represented as Fourier series. By this fact, a density function can be estimated by Fourier series as estimator of continues function on L2[-p,p]. Further, this function estimator will be used to derive process parameters that needed on the control quality chart design  
GRAFIK PENGENDALI NON PARAMETRIK EMPIRIK Santoso, Rukun
MEDIA STATISTIKA Vol 1, No 2 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (156.226 KB) | DOI: 10.14710/medstat.1.2.83-90

Abstract

Shewhart control chart is constructed base on the normality assumption of process.  If the normality is fail then the empirical control chart can be an alternative solution. This means that the control chart is constructed base on empirical density estimator. In this paper the density function is estimated by kernel method.  The optimal bandwidth is selected by leave one out Cross Validation method. The result of empirical control chart will be compared to ordinary Shewhart chart.   Key words : Control chart, Kernel, Cross Validation
METODE NONLINEAR LEAST SQUARE (NLS) UNTUK ESTIMASI PARAMETER MODEL WAVELET RADIAL BASIS NEURAL NETWORK (WRBNN) Santoso, Rukun; Sudarno, Sudarno
MEDIA STATISTIKA Vol 10, No 1 (2017): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (629.895 KB) | DOI: 10.14710/medstat.10.1.49-59

Abstract

The use of wavelet radial basis model for forecasting nonlinear time series is introduced in this paper. The model is generated by artificial neural network approximation under restriction that the activation function on the hidden layers is radial basis. The current model is developed from the multiresolution autoregressives (MAR) model, with addition of radial basis function in the hidden layers. The power of model is compared to the other nonlinear model existed before, such as MAR model and Generalized Autoregressives Conditional Heteroscedastic (GARCH) model. The simulation data which be generated from GARCH process is applied to support the aim of research. The sufficiency of model is measured by sum squared of error (SSE). The computation results show that the proposed model has a power as good as GARCH model to carry on the heteroscedastic process.Keywords:Wavelet, Radial Basis, Heteroscedastic Model, Neural Network Model.
RELIABILITAS DAN AVAILABILITAS SISTEM TIGA KOMPONEN TERSUSUN PARALEL BERSERI Sudarno, Sudarno; Santoso, Rukun; Anugraheni, Avida
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 2 (2018): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

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Abstract

Reliability is the ability of a product or an item to maintain the required function of a specified period of time under given operating conditions. Availability is a measure of system performance and measures the combined effect of reliability, maintenance and logistic support on the operational effectivesness of the system. The system was formedby some components. This system could be broken, then it could not be operated. In order to system could operate again, it should be repaired. This system consist of three components, such that component-1 is a processor core, component-2 is interface input/output, and component-3 is memory. The system was arranged by parallel-seri.This paper use generation data. Data are failure time and repair time of components of system, respectively. Therefore, research variables are failure time and repair time of all component of system. The aim of this research is finding the mean time to failure and the mean time to repair components, reliability of system, and availability of system.The research result of reliability of system is 0.9998 while availability of system is 0.9987. These results could be concluded that system have best quality and high performing. Generally, if reliability value was higher then quality of system more perfect and if availability value was higher then perform of system was better.  Keywords : Reliability, availability, mean time to failure, mean time to repair.