Browsing by Author "Bustami"
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Item ANALISIS PERAMALAN DENGAN MENGGUNAKAN METODE PEMULUSAN EKSPONENSIAL TUNGGAL(2016-04-27) Rahmattia, Annisa; Bustami; Gamal, M. D. HThis article discusses the forecasting method that is used to predict the value of the future. This forecasting uses a single exponential smoothing method which has one smoothing parameter that satis es a value between 0 and 1 obtained through trial and error. Then linear programming is used for determining an optimal smoothing parameter value. This method simply sets the forecast value of future period with a measure of forecasting error using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This forecasting method is implemented in a case of predicting the sale price of bunch of palm at a company in Riau.Item APLIKASI MODEL BLACK-LITTERMAN DALAM MENENTUKAN OPTIMISASI PORTOFOLIO INVESTASI(2016-02-04) Indrasari, Juni Meli; Nababan, Tumpal P; BustamiThis article studies the weighted Black-Litterman’s model in finding the optimization of portfolio invesment. The Black-Litterman’s model obtained by Bayesian approach is used to obtain the weighted values of each asset portfolio. Based on the weighted values of each asset the portfolio return is obtained and then is used in determining optimal value of portfolio.Item INTERVAL KEPERCAYAAN UNTUK PERBEDAAN KOEFISIEN VARIASI DARI DISTRIBUSI LOGNORMAL(2014-03-25) Pebriyani, I.; Bustami; Sugiarto, S.This article discusses confidence interval for the difference of variation coefficients for lognormal distribution using the pivotal quantity, where pivotal quantity for the variation coefficients is unavailable, but there is a parameter having pivotal quantity in coefficients of variation i.e 2 . Therefore, 2 is constructed for pivotal quantity coefficients of variation using the Generalized Pivotal Approach (GPA). This confidence interval is shown in the term of coverage probabilities by implementing simulation studies with Matlab 7.6.0Item METODE PEMULUSAN EKSPONENSIAL WINTER UNTUK PERAMALAN(2016-02-04) Manurung, Arganata; BustamiThis paper studies the forecasting method namely method of Winter's exponential smoothing. This forecasting method is used to forecast the number of departure and arrival of domestic passengers at Sultan Syarif Kasim II Airport Pekanbaru in 2015 by considering the seasonal factor from time series data used. Then the evaluation of forecasting error is carried out by using the mean absolute percentage error (MAPE) to obtain good forecast results.Item METODE PERAMALAN HOLT-WINTERS DENGAN MEMPERHATIKAN EFEK LEBARAN(2016-02-04) Irawansyah, Hadi; Gamal; BustamiThis article discusses the Holt-Winters forecasting method that takes into account the effect of Eid. This method is used to predict total domestic passenggers departing in 5 major ports in Indonesia. Then a comparison is carried out between the method of Holt-Winters and Holt-Winters methods that take into account the effect of Eid. The comparison is done by comparing the mean square error obtained by both methods of forecasting.Item PENAKSIR BAYES UNTUK PARAMETER DISTRIBUSI EKSPONENSIAL BERDASARKAN FUNGSI KERUGIAN KUADRATIK DAN FUNGSI KERUGIAN ENTROPI(2016-05-23) Nengsih, Nadya Zulfa; BustamiThis paper discusses the estimator for parameter of exponential distribution using Bayesian statistics. Prior distribution used is the extension of Jeffrey’s prior. Bayes estimators are obtained by quadratic loss function and entropy loss function. Bayes estimators under quadratic loss function and entropy loss function are biased estimators. Mean squared errors of Bayes estimators are obtained using simulation. Simulation results show that Bayes estimator under entropy loss function with proper choise of γ and depending on the value of c is more efficient than Bayes estimator under quadratic loss function.Item PENAKSIR PARAMETER DISTRIBUSI EKSPONENSIAL PARETO DENGAN METODE MOMEN DAN METODE MAKSIMUM LIKELIHOOD(2016-02-04) Sari, Mayang Novhita; Bustami; Sugiarto, SigitThis articlediscusses the parameter estimator of exponential Pareto distribution. Parameters p are estimated by constans and , using the method of moments and method of maximum likelihood. Method of moments estimator is unbiased estimator and themethod of maximum likelihood estimator is unbiased estimator for 1 andbiasfor 1. Variance of method of moments and mean square error of method of maximum likelihood are obtained using numerical simulation. The simulation results show that the method of maximum likelihood estimator better than the method of moments estimator.Item PENAKSIR RASIO UNTUK RATA-RATA POPULASI PADA SAMPLING ACAK BERSTRATA ADAPTIF CLUSTER(2014-03-25) Ardini, Dita; Efendi, Rustam; BustamiThis article discusses two ratio estimators for the population mean in stratified adaptive cluster random sampling, the ratio estimator for the population mean and ratio estimator for the population mean, both proposed by Nipaporn Chutiman published in Thailand Statistician Journal [1]. All estimators are biased estimators. Then the mean square errors are determined for each estimator. Futhermore, the mean square error of each estimator will be compared to obtain an efficient ratio estimator. An numerical example is given at the end of the discussion.Item TAKSIRAN PARAMETER BENTUK, LOKASI DAN SKALA DARI DISTRIBUSI WEIBULL(2016-04-26) Rukiyah, Siti; Bustami; Sugiarto, SigitThis paper is a review of an article proposed by El-Mezouar [4], the methods of moment based method of Cran used to estimate the parameters of the Weibull distribution three parameters where the location parameters is with non-negative. Method of moments based Cran method using a simple procedure with regard location parameters is zeros.Item TAKSIRAN PARAMETER DISTRIBUSI WEIBULL DENGAN MENGGUNAKAN METODE MOMEN DAN METODE KUADRAT TERKECIL(2013-03-26) Fanila, Hesty; Adnan, Arisman; BustamiThe methods of moments and least square for estimating parameters of the Weibull distribution that proposed by Razali, et al [6] have been reviewed. Since the estimator of parameters are biased then Mean Square Error for comparing these two estimators was used. Simulation has been conducted using several sample sizes and different parameters. Our study support Razali’s result that the method of moments is more efficient than that least square methodItem TAKSIRAN YANG LEBIH EFISIEN UNTUK PARAMETER PADA DISTRUSI WEIBULL(2013-03-25) Wati, Erma Kusuma; Sugiarto, Sigit; BustamiIn this paper the authors use the method of maximum likelihood and Bayesian methods to estimate parameters of the Weibull distribution. Prior used for the Bayesian method is Jeffery prior information. The loss function used is the quadratic loss function. Then the computing was done by comparing of the maximum likelihood method and Bayesian method to find the smallest Mean Square Error. The comparison among the discussed methods is done by simulation methods. The estimated parameters of Weibull distribution obtained from the maximum likelihood is the best compared to Bayes using Jeffery prior