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Jurusan Statistika FSM Undip

Published : 44 Documents
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JUAL BELI SISTEM ONLINE DI ERA DIGITAL MENURUT HUKUM ISLAM DAN HUKUM POSITIF DI INDONESIA Payitno, Isnu Harjo; Sulasmi, Andi; Tarno, Tarno; Saefudin, Encep; Dadang, Rahmat
Abdi Laksana Vol 1, No 2 (2020): Jurnal Pengabdian kepada Masyarakat
Publisher : Abdi Laksana

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Abstract

The purpose of the implementation of Community Service entitled "Buying and Selling Online Systems According to the View of Islamic Law and Positive Law" is as an effort to fulfill the obligations of the Three Principles of Higher Education as mandated in Article 1 paragraph 9, Law No. 12 of 2012 concerning Higher Education. Based on the foregoing, Community Service (PKM) activities in the form of providing knowledge and understanding of the Online Sale and Purchase of Systems. The method used in this Community Service is in the form of counseling on the understanding of the Concept of Buying and Selling Online System accompanied by the view of Islamic Law (Sharia) and Positive Law which covers both Criminal, Civil and existing Regulations. The Community Service Result obtained is a basic understanding and desire of PKM objects to be able to know the signs and anticipate the negative effects of evil behavior.Keywords: Dedication, Buying and Selling Online, Islamic Law, Positive Law
PEMODELAN RETURN PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH ASIMETRIS Arifin, Muhammad; Tarno, Tarno; Warsito, Budi
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Investment in stocks is an alternative for investors and companies to obtain external funding sources. In the investment world there is a strong relationship between risk and return (profit), if the risk is high then return will also be high. Risks can be minimized by performing stock portfolio. Stock is the time series data in the financial sector, which usually has a tendency to fluctuate rapidly from time to time so that variance of error is not constant. Time series model in accordance with these condition is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). This research will apply asymmetric GARCH covering Exponential GARCH (EGARCH), Threshold GARCH (TGARCH), and Autoregressive Power ARCH (APARCH) in stock data Indocement Tunggal Tbk (INTP), Astra International Tbk (ASII), and Adaro Energy Tbk (ADRO) commencing from the date of March 1, 2013 until February 29, 2016 during an active day (Monday to Friday). The purpose of this research is to predict the value of the volatility of a portfolio of three assets stocks. The best models used for forecasting volatility in asset stocks which have asymmetric effect is ARIMA ([13],0,[2,3]) EGARCH (1,1) on a single asset data INTP, ARIMA ([2],0,[2,3]) EGARCH (1,1) on the 2 asset portfolio data ASII INTP, and ARIMA ([3],0,[2]) EGARCH (1,1) on the 3 asset portfolio data INTP-ASII-ADRO.Keywords: Stocks, Portfolio, Return, Volatility, Asymmetric GARCH.
PEMILIHAN INPUT MODEL ANFIS UNTUK DATA RUNTUN WAKTU MENGGUNAKAN METODE FORWARD SELECTION DILENGKAPI GUI MATLAB (STUDI KASUS: JUMLAH PENUMPANG KERETA API DI WILAYAH JAWA NON JABODETABEK) Valentina, Tiara Sukma; Tarno, Tarno; Prahutama, Alan
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

One of the methods that is commonly used to identify a time series model and input ANFIS (Adaptive Neuro Fuzzy Inference System) model is PACF plot. The PACF plot shows the correlation between current observations and previous observations visually. Formally there are several methods that are known to effectively identify ANFIS inputs, one of which is the Forward Selection regression method. With the same concept as PACF, the process of selecting ANFIS inputs using the Forward Selection method is based on the order of the correlatiom between the predictors of the response which is indicated by the magnitude of the correlation coefficient. This study discusses the Forward Selection method in simulation data that has stationary characteristics, stationary with outliers, non stationary, non stationary with outliers and implements data on the number of train passengers in the Non Jabodetabek Java region. ANFIS modeling on data of the number of train passengers in the Non Jabodetabek Java region produces AIC of 15,5617, MAPE of 8,5093% and RMSE of 571,33691. The result of this study is equipped with a GUI which is useful as a tool to facilitate users in processing data.Keywords : PACF Plot, Forward Selection, ANFIS, non stasionary, outlier
PEMODELAN REGRESI SPLINE TRUNCATED UNTUK DATA LONGITUDINAL ( STUDI KASUS : HARGA SAHAM BULANAN PADA KELOMPOK SAHAM PERBANKAN PERIODE JANUARI 2009 – DESEMBER 2015 ) Fadhilah, Khoirunnisa Nur; Suparti, Suparti; Tarno, Tarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Stocks are securities that can be bought and sold by individuals or institutions as a sign of ownership of any person nor bussines entity within a company. From the value of market capitalization, the stock is divided into 3 groups: large capitalization (big-cap), medium capitalization (mid-cap), and small capitalization (small-cap). The stocks has been fluctuated up and down because of several factors, one of them is inflation. Longitudinal data are observations made of n subjects that mutually independent with each subject which observed repeatedly in different period of time mutually dependent. Modelling longitudinal data of stock prices do with truncated spline nonparametric regression approach. The best model of spline depends on the determination of the optimal knot points which has minimum value of Generalized Cross Validation (GCV). The best of truncated spline regression is spline order 2 with 3 knot points for each of the subjects on longitudinal data. By using the model, the value of MAPE for each subject is 29,93% for PT Bank Mandiri (Persero) Tbk., 16,67% for PT Bank Bukopin Tbk., and 12,99% for PT Bank Bumi Arta Tbk.. Keywords: stocks, longitudinal data, truncated spline, GCV
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).
PERHITUNGAN VALUE AT RISK MENGGUNAKAN MODEL INTEGRATED GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (IGARCH) (STUDI KASUS PADA RETURN KURS RUPIAH TERHADAP DOLLAR AUSTRALIA) Febriana, Dian; Tarno, Tarno; Sugito, Sugito
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Foreign exchange trading can be an alternative investment due to the rapid movement of the exchange rate and its liquid characteristic. Measurement of risk is important because investment is related to substantial funds. One of the popular methods of risk measurement is Value at Risk (VaR) method. In financial time series, data usually have a variance that is not constant (heteroscedastisity). To overcome these problems, ARCH and GARCH models are used. One type of ARCH / GARCH namely Integrated Generalized Autoregressive Conditional Heteroscedasticity (IGARCH). The purpose of this study is modeling the IGARCH volatility and to calculate VaR based on the estimate volatility of the  exchange rate return data rupiah against the Australian dollar. This study use daily selling rate data of the rupiah against the Australian dollar from 1 June 2012 until February 28, 2014. The best IGARCH model used for forecasting volatility of exchange rate return data Rupiah against the Australian dollar is the ARIMA model ([10], 0, [19]) IGARCH (1,1) because it has the smallest AIC value. The estimation volatility forecasting results obtained from the IGARCH (1,1) is used to calculate the value at risk on 5 periods ahead with one day holding period and a confidence level of 95%. Value at Risk to be around 0.95% to 1.07% with the highest VaR on 3rd March 2014 and the lowest VaR on 7th March 2014. Keywords : Exchange rate, Volatility, Integrated  Generalized Autoregressive Conditional Heteroscedasticity (IGARCH), Value at Risk (VaR)
OPTIMASI VALUE AT RISK PADA REKSA DANA DENGAN METODE HISTORICAL SIMULATION DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Monica, Christa; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Value at Risk (VaR) is a method used to measure financial risk within a firm or investment portfolio over a specific time period at certain confidence interval level. Historical Simulation is used in this research to compute VaR of stock mutual fund at 5% confidence interval level, with one day time period and Rp 100.000.000,00 startup investment fund. Historical Simulation ia a non parametric method where the formula doesn?t require any asumption. Portfolio optimization is done by calculating the weight of allocation fund for each asset in the portfolio using Mean Variance Efficient Portfolio (MVEP) method. The data in this research are divided into four mutual fund asset. To make VaR become easier for people to understand, an application is made using GUI in Matlab. The smallest risk value for single investment asset is obtained by Valbury Equity I stock mutual fund and the smallest risk value for two-asset portfolio is obtained by the combination assets of Pacific Equity Fund and Valbury Equity I. Meanwhile for three-asset portfolio, the combination assets of Pacific Equity Fund, Valbury Equity I, and Millenium Equity Prima Plus have the smallest risk value. The test result of VaR with Basel Rules shows that the usage of VaR is legitimate to measure loses potency in mutual fund investment.Keywords: Value at Risk (VaR), Historical Simulation, Mutual Fund, Risk.
PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (APLIKASI PADA DATA HARGA BERAS KUALITAS RENDAH DI INDONESIA PERIODE JANUARI 2013 – FEBRUARI 2019) Fatimah, Khusnul Umi; Tarno, Tarno; Hoyyi, Abdul
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting
PEMODELAN LAJU INFLASI DI PROVINSI JAWA TENGAH MENGGUNAKAN REGRESI DATA PANEL Apriliawan, Dody; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

Panel regression is a regression which is a combination of cross section and time series. To estimate the panel regression there are 3 approaches, the common effect model (CEM), the fixed effect model (FEM) and the random effect model (REM). In the CEM, the parameters were estimated using the Ordinary Least Square (OLS). In the FEM, the parameters estimated by OLS through the addition of dummy variables. At REM, error is assumed random and estimated by the method of Generalized Least Square (GLS). This study aims to analyze the factors that influence inflation in the Central Java province using panel regression. Based on test result of panel regression, the appropriate model is the CEM. The parameters of model are estimated by using OLS the cross section weights. The model show that the Consumer Price Index (CPI), Minimum Salary of City/Regency (MSCR) and the economic growth significantly effect on percentage of inflation in Central Java Province.
PEMILIHAN THRESHOLD OPTIMAL PADA ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN METODE CROSS VALIDASI Suparti, Suparti; Tarno, Tarno; Hapsari, Paula Meilina Dwi
MEDIA STATISTIKA Vol 2, No 2 (2009): 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 (341.567 KB) | DOI: 10.14710/medstat.2.2.56-69

Abstract

If x is a predictor variable and y is a response  variable of  the regression model y = f (x)+ Î with  f is a regression function which not yet been known and Î is independent random variable with mean 0 and variance , hence function f can be estimated by parametric and nonparametric approach. In this paper function f is estimated with a nonparametric approach. Nonparametric approach that used is a wavelet shrinkage or a wavelet threshold method. In the function estimation with a wavelet threshold method,  the value of  threshold has  the most important role to determine  level of smoothing estimator. The small threshold give function estimation very no smoothly, while  the big value of threshold give function estimation very smoothly. Therefore the optimal value of threshold should be selected to determine the optimal function estimation. One of the methods to determine the optimal value of threshold by minimize a cross validation function. The cross validation method that be used is two-fold cross validatiaon. In this cross validation, it compute the predicted value by using a half of data set. The original data set is split  into two subsets of equal size : one containing only the even indexed data, and the other, the odd indexed data. The odd data will be used to predict the even data, and vice versa. Based on  the result of data analysis, the optimal threshold with cross validation method is not uniq, but they give the  uniq of wavelet thersholding regression estimation.   Keywords : Nonparametric Regression, Wavelet Threshold Estimator, Cross Validation.