Browsing by Author "Bustami"

Now showing 1 - 12 of 12
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    ANALISIS PERAMALAN DENGAN MENGGUNAKAN METODE PEMULUSAN EKSPONENSIAL TUNGGAL
    (2016-04-27) Rahmattia, Annisa; Bustami; Gamal, M. D. H
    This 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.
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    APLIKASI MODEL BLACK-LITTERMAN DALAM MENENTUKAN OPTIMISASI PORTOFOLIO INVESTASI
    (2016-02-04) Indrasari, Juni Meli; Nababan, Tumpal P; Bustami
    This 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.
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    METODE PEMULUSAN EKSPONENSIAL WINTER UNTUK PERAMALAN
    (2016-02-04) Manurung, Arganata; Bustami
    This 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.
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    METODE PERAMALAN HOLT-WINTERS DENGAN MEMPERHATIKAN EFEK LEBARAN
    (2016-02-04) Irawansyah, Hadi; Gamal; Bustami
    This 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.
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    MODEL HYBRID SINGULAR SPECTRUM ANALYSIS DAN NEURAL NETWORK UNTUK PERAMALAN KENAIKAN NILAI INFLASI DI INDONESIA
    (Elfitra, 2023-12) Dwifattah, Muhammad Rizki; Bustami
    Current economic developments cause increasing inflation rates in a country. One of the statistical methods used to determine the increase in inflation values is forecasting using a non-parametric time series model. This research was carried out using Singular Spectrum Analysis and Neural Network as a non-parametric forecasting method with monthly data on inflation values in Indonesia from January 2003 - December 2022. This analysis was carried out by forming a square matrix from the research data so that eigenvalues and eigenvectors were obtained in each matrix. as many as 50. In the calculations, the forecast results obtained for the next 5 month period show insignificant increases and decreases. Based on the accuracy results, an error was obtained using MAPE with forecasting results for the inflation value of 9%, which can be said to be in the very good category.
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    PENAKSIR PARAMETER DISTRIBUSI EKSPONENSIAL PARETO DENGAN METODE MOMEN DAN METODE MAKSIMUM LIKELIHOOD
    (2016-02-04) Sari, Mayang Novhita; Bustami; Sugiarto, Sigit
    This 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.
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    PENAKSIR RASIO UNTUK RATA-RATA POPULASI PADA SAMPLING ACAK BERSTRATA ADAPTIF CLUSTER
    (2014-03-25) Ardini, Dita; Efendi, Rustam; Bustami
    This 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.
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    PENERAPAN DIAGRAM KONTROL DEMERIT PADA PEGENDALIAN KUALITAS PRODUK CACAT ROTTE BAKERY
    (Elfitra, 2023-12) Dharma, Pandu Satria; Bustami
    This study discusses the application of demerit control charts to quality control of Rotte Bakery defective products which consists of three types of bread, namely Fit O Mini, Cokelat Spesial, and Fit O Vanilla. The purpose of this study is to see the quality control of defective products during the production process through visualization in the form of a demerit control chart. The demerit control chart begins by determining the weight for each type of bread, then calculating the demerit value for each subgroup, followed by determining the centerline, upper center line, and lower center line, and ends by visualizing the number of defective products using the demerit control chart. The results showed that the quality of the Fit O Mini, Chocolate Special, and Fit O Vanilla products was still under control. This can be seen in the degree of membership in each subgroup which is still present in the upper and lower limit intervals of the demerit diagram.
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    PENERAPAN METODE DEKOMPOSISI UNTUK PERAMALAN HARGA SAHAM PT BANK CENTRAL ASIA TBK
    (Elfitra, 2023-12) Tampubolon, Omar Farrakhan; Bustami
    Singular Spectrum Analysis (SSA) is a non-parametric time series analysis technique used for forecasting. The advantage of this method is that it does not require the assumption of stationarity, thus making SSA a good time series data analysis technique to describe trends and other components that have a simple structure. The data used for forecasting using the Riau Province Interest Rate in the period January 2013 to January 2023. The analysis carried out is in the form of a matrix to find the Eigen value and Eigen Vector. The right SSA model in this case is obtained by window length 37 and number of groups 3 with MAPE 4.569288%. The accuracy of this SSA method is considered very good for forecasting such as the Interest Rate data in Riau Province.
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    TAKSIRAN PARAMETER BENTUK, LOKASI DAN SKALA DARI DISTRIBUSI WEIBULL
    (2016-04-26) Rukiyah, Siti; Bustami; Sugiarto, Sigit
    This 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.
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    TAKSIRAN PARAMETER DISTRIBUSI WEIBULL DENGAN MENGGUNAKAN METODE MOMEN DAN METODE KUADRAT TERKECIL
    (2013-03-26) Fanila, Hesty; Adnan, Arisman; Bustami
    The 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 method
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    TAKSIRAN YANG LEBIH EFISIEN UNTUK PARAMETER PADA DISTRUSI WEIBULL
    (2013-03-25) Wati, Erma Kusuma; Sugiarto, Sigit; Bustami
    In 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