Rita Rahmawati
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

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PERBANDINGAN ANALISIS DISKRIMINAN FISHER DAN NAIVE BAYES UNTUK KLASIFIKASI RISIKO KREDIT (Studi Kasus Debitur di Koperasi Jateng Amanah Mandiri Cabang Sukorejo Kendal) Abdur Rofiq; Triastuti Wuryandari; Rita Rahmawati
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (963.688 KB) | DOI: 10.14710/j.gauss.v5i1.10907

Abstract

Credit is a form of money lending to debitors conducted by financial institutions such as cooperatives. In practice there are obstacles in the form of bad credit. Analyze by Fisher discriminant analysis method and Naive Bayes is used to classify the debitors fall into the category bad debitorr or not. This study uses data from  the Debitors of Cooperative of Central Java Amanah Independent in Sukorejo Kendal Branch. The data obtained is used for classification by Fisher discriminant analysis and Naive Bayes method. Data obtained has  multivariate normal distribution, has the same of variance-covariance matrix and has metric scale. Fisher discriminant analysis and Naive Bayes calculated and compared to the level of accuracy. From this research, the degree of accuracy of each method, namely 90% for Fisher Discriminant Analysis and 83.33% for the Naive Bayes. Having tested using the proportion test, Fisher discriminant analysis method is no different accuracy when compared with Naive Bayes to classify credit risk. Keywords: debitors, credit risk, Fisher discriminant analysis, Naive Bayes.
REGRESI KOMPONEN UTAMA ROBUST S-ESTIMATOR UNTUK ANALISIS PENGARUH JUMLAH PENGANGGURAN DI JAWA TENGAH Jeffri Nelwin J. O. Siburian; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.68 KB) | DOI: 10.14710/j.gauss.v8i4.26724

Abstract

Robust principal component regression s-estimator is principal component regression that applies robust approach method at principal component analysis and s-estimator at principal component regression analysis. The aim of robust principal component regression s-estimator is to overcome multicollinearity problems in multiple linier regression Ordinary Least Square (OLS) and to overcome outlier problems in principal component regression so get the most effective model. Minimum Volume Ellipsoid (MVE) is one of the robust approach methods that applied when doing principal component analysis and S-Estimator is one of the estimation methods that applied when doing principal component regression analysis. The case in this study is the factors that influence the Number of Unemployment in Central Java in 2017. The model that provides the most effective result to handling multicolliniearity and ouliers in the case study  Number of Unemployment in Central Java in 2017 is using robust principal component regression MVE-(S-Estimator) with Adjusted R2 value of 0.9615 and RSE value of 0.4073. Keywords: Robust Principal Component Regression S-Estimator, Multicollinearity, Outliers, Minimum Volume Ellipsoid (MVE), Number of Unemployment.
VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL Dina Rahma Prihatiningsih; Di Asih I Maruddani; Rita Rahmawati
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.v9i3.28913

Abstract

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.
PENGELOMPOKAN TITIK GEMPA DI PULAU SULAWESI MENGGUNAKAN ALGORITMA ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise) Denny Jales Manalu; Rita Rahmawati; Tatik Widiharih
Jurnal Gaussian Vol 10, No 4 (2021): 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.v10i4.29499

Abstract

Earthquake is a natural disaster which is quite serious in Indonesia, especially on Sulawesi Island. Earthquake is fearful because it can’t be predicted when it will come, where it will come, and how strong the vibration, that often causes fatal damage and casualties. In effort to minimize losses caused by earthquake, it is necessary to divide areas which are easily affected by earthquake. One of the methods that can be used in dividing the area is by using the clustering technique. This research by using a clustering method with the ST-DBSCAN (Spatial Temporal-Density Based Spatial Clustering Application with Noise) algorithm on dataset of earthquake points in Sulawesi Island in 2019. This method by using the spatial distance parameters (Eps1 = 0.45), the temporal distance parameters (Eps2 = 7), and minimum number of cluster members (MinPts = 4), resulting in a total of 60 clusters with 8 large clusters and 216 noises 
PEMODELAN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI SPLINE MULTIVARIABEL Ihdayani Banun Afa; Suparti Suparti; Rita Rahmawati
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.157 KB) | DOI: 10.14710/j.gauss.v7i3.26659

Abstract

The Composite Stock Price Index (CSPI) is a composite index of all types of shares listed on the stock exchange and their movements indicate the conditions occurring in the stock market. CSPI movement is an important indicator for investors to determine whether they will sell, hold, or buy a stock. One of the factors that influence the movement of CSPI is Inflation (X1), Exchange Rate (X2) and SBI rate (X3). This study aims to obtain the best CSPI model using a multivariable nonparametric spline regression approach. The approach is done by nonparametric regression because the regression curve obtained does not show a certain relationship pattern. Spline is very dependent on the order and location of the knot point. The best spline model is the model that has the minimum MSE (Mean Square Error) value. In this study, the best spline regression model is when X1 is 4 order, X2 is 2 order and X3 is 2 order. The number of knots on X1 is 1 knot at 8.22, X2 is 2 knots at 13066.82 and 13781.75 While X3 is 2 knots at 6.6 and 6.67 with value of MSE equal to 6686.85.Keywords: Composite Stock Price Index, Multivariable Spline Regression, MSE
PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh) Enggartya Andini; Sudarno Sudarno; Rita Rahmawati
Jurnal Gaussian Vol 10, No 1 (2021): 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.v10i1.30939

Abstract

An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a Tj2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no Tj2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MCpm value is 0.516797 and the MCpmk value is 0.437807, the value indicates a process capability index value of less than 1. Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.
PEMILIHAN PERUMAHAN TERFAVORIT MENGGUNAKAN METODE VIKOR DAN TOPSIS DENGAN GUI MATLAB (Studi Kasus: Perumahan Mijen Semarang) Alika Ramadhani; Rukun Santoso; Rita Rahmawati
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (710.252 KB) | DOI: 10.14710/j.gauss.v8i3.26678

Abstract

The increase in the population of Semarang has an impact on the increasing demand for residential housing. Unfortunately, the limitations of the area became an obstacle in Semarang to develop residential areas. This development of residential housing in Semarang leads to suburban such as Mijen. The method that can be used to choose favorite housing is Visekriterijumsko Kompromisno Rangiranje (VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both methods can be applied to solve Multiple Criteria Decision Making (MCDM) issue. This study has 8 alternatives of residential housing in Mijen with 5 criteria such as Price, Payment Method, Building Specifications, Housing Facilities, and Location. This research was design with Graphical User Interface (GUI) Matrix Laboratory (MATLAB) as computing tool. VIKOR and TOPSIS method on this research, obtained the same result that the most favorite residential housing is A5. Keywords: Housing, SPK, VIKOR, TOPSIS, GUI
PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB Umiyatun Muthohiroh; Rita Rahmawati; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 3 (2021): 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.v10i3.32805

Abstract

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).  
PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Ekky Rosita Singgih Wigati; Budi Warsito; Rita Rahmawati
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (462.117 KB) | DOI: 10.14710/j.gauss.v7i1.26636

Abstract

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE 
PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB Umiyatun Muthohiroh; Rita Rahmawati; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 4 (2021): 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.v10i4.33098

Abstract

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).