ESTIMASI: Journal of Statistics and Its Application
ESTIMASI: Journal of Statistics and Its Application, is a journal published by the Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University. ESTIMASI is a peer – reviewed journal with the online submission system for the dissemination of statistics and its application. The material can be sourced from the results of research, theoretical, computational development and all fields of science development that are in one group.
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107 Documents
<![CDATA[Model Regresi Data Panel Pada Kasus Infeksi Saluran Pernapasan Akut (ISPA) di Provinsi Nusa Tenggara Timur ]]>
<![CDATA[Indah Magfirrah Jamaludin]]>;
<![CDATA[Astri Atti]]>;
<![CDATA[Maria A. Kleden]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 1, Januari, 2021 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v2i1.12504
<![CDATA[Acute respiratory infection (ARI) is an infectious desease cause by bacteria or viruses that attack the respiratory organs. This research aims to determine the best panel data regression model in the case of the factors that influence the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Response variable used is the number of ARI patients. Independent variables were observed among others, low birth weight, malnutrition, immunization, exclusive breastfeeding, and vitamin A in 22 districts or city in East Nusa Tenggara. The results showed that the Random Effect Models eliminate outlier data on response variable is a model that can describe the influence of independent variables on the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Variables that influence of ARI are malnutrition and exclusive breastfeeding with a coefficient of determination (R) of 9,2%.]]>
<![CDATA[Perancangan Aplikasi Peramalan untuk Metode Exponential Smoothing Menggunakan Aplikasi Lazarus (Studi Kasus: Data Konsumsi Listrik Kota Samarinda) ]]>
<![CDATA[Hairi Septiyanor]]>;
<![CDATA[Syaripuddin Syaripuddin]]>;
<![CDATA[Rito Goejantoro]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 2, Juli, 2021 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v2i2.13364
<![CDATA[Exponential smoothing is forecasting method used to predict the future. Lazarus is an open source software based on free pascal compiler. at this research, program Lazarus be design used exponential smoothing method to predict electricity consumption data in Samarinda City from September to November 2018. Purposed of this researched is to determine the procedure of building an exponential smoothing forecasting application and obtained forecasting result using the built application. Procedure of built the application are designed interface, designed properties and filled coding. The optimum smoothing parameters were obtained used the golden section method. Based on the analysis, electricity consumption data in Samarinda City shows a trend pattern, then the forecasting was used double exponential smoohting (DES) method are DES Brown and DES Holt. The best forecasting method for at this researched is DES Holt, because DES Holt method produced MAPE 0,0659% less than DES Brown method produced MAPE 0,0843%.]]>
<![CDATA[Faktor-Faktor Yang Mempengaruhi Penyapihan Bayi Umur Kurang Dari 6 Bulan Melalui Studi Cross Section ]]>
<![CDATA[Dahniar Dahniar]]>;
<![CDATA[Nurdiana Nurdiana]]>;
<![CDATA[Abdul Halim]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 2, Juli, 2021 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v2i2.14448
<![CDATA[Weaning too early can affect the growth of the baby and the mother often ignores the nutritional needs of the baby. In addition, malnutrition is more common today than during the first 4-6 months of life. This is because many families do not understand the special needs of babies and are unable to provide food with good nutritional value. The type of research used is a survey with a cross-sectional study approach. The population is all mothers who have babies aged 6 months and the sample is all mothers who have babies aged 6 months. The sampling technique was simple random sampling. The results showed that there was a significant effect between mothers who did weaning for less than 6 months with education = 0.006. There is a significant effect between mothers who do weaning for less than 6 months with employment status = 0.008. There is a significant effect between mothers who do weaning less than 6 months with birth spacing = 0.007.]]>
<![CDATA[Analisis Diskriminan Linear Robust Dengan Metode Winsorized Modified One-Step M-Estimator ]]>
<![CDATA[Mega Selvia Tjahaya]]>;
<![CDATA[Raupong]]>;
<![CDATA[Georgina Maria Tinungki]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11302
<![CDATA[Discriminant analysis is a method used to classify an individual (object) into a group. Discriminant analysis is divided into classical linear discriminant analysis and classical quadratic discriminant analysis. Discriminant analysis must fulfilled the assumptions of normality and homogeneity of the variance-covariance matrix, however this method is very sensitive to data contains outliers. Robust linear discriminant analysis with the winsorized modified one-step M-estimator(WMOM) approach is a method that can resolve outliers data. WMOM works by trimming these outliers then replacing the outliers with the highest or lowest value of the remaining data by using criteria trimming MOM. This study aims to obtain a linear robust discriminant function with the WMOM method using the Sn scale on diabetes and prediabetes data for the period December 2016-January 2017. Based on the results of the analysis and discussion of this method, the discriminant function is obtained with a classification error rate of 16.67%. Keywords: Diabetes, One-Step M-estimator, Prediabetes, Robust Linear Discriminant Analysis, Winsorized Modified.]]>
<![CDATA[Rancangan Faktorial Model Campuran Menggunakan Metode Maksimum Likelihood ]]>
<![CDATA[Andi Tenri Riski Amalia]]>;
<![CDATA[Raupong Raupong]]>;
<![CDATA[Andi Kresna Jaya]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11406
<![CDATA[Variance is the amount of statistics which measures how far a set of numbers in observation are spread out from its mean. In experimental design, variance are caused by the effect of treatment, block and error of experimental can be estimated by variability of error that commonly referred to variance component. In this study, the maximum likelihood method with Hartley Rou modification was used followed by the Newton Raphson method which was applied to a complete randomized block factorial design mixed model with factor A being fixed and factor B being random. The results of this study for rice production data showed that there is a significant effect on the interaction of genotype and location on rice production. The estimated value of the variance component obtained indicates that there are variations in the influence of location factors, and genotype and location interaction factors on rice production.]]>
<![CDATA[Model Regresi Bivariate Zero-Inflated Poisson Pada Kematian Ibu dan Bayi ]]>
<![CDATA[Andi Isna Yunita]]>;
<![CDATA[Andi Kresna Jaya]]>;
<![CDATA[Georgina Maria Tinungki]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11557
<![CDATA[Overdispersion is a condition with greater variance than the mean. One of the causes overdispersion is more zero-value observations so the Zero-Inflated Poisson (ZIP) regression model can be used. As for modeling a pair of discrete data is correlated and overdispersion, it can be used the Bivariate Zero-Inflated Poisson (BZIP) regression model. The BZIP regression model is a model with response variables with mixed distributions between Bivariate Poisson distribution and a point probability at (0,0). Parameters of the BZIP regression model are estimated using maximum likelihood estimation (MLE) with expectation maximization (EM) algorithm. This research was applied to data on number of maternal and infant mortality in the city of Makassar in 2017. The result obtained is the AIC value of the BZIP regression model is 170.976 smaller than the Bivariate Poisson regression model is 198.120. This shows that the BZIP regression model is better used for data with overdispersion.]]>
<![CDATA[Model ARIMA dengan Variabel Eksogen dan GARCH pada Data Kurs Rupiah ]]>
<![CDATA[Ririn Arianti]]>;
<![CDATA[Sitti Sahriman]]>;
<![CDATA[La Podje Talangko]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11603
<![CDATA[Autoregressive integrated moving average with exegenous variable (ARIMAX) model is the development of ARIMA model with addition of other time series data as exogenous variable that affect the dependent variable. ARIMAX model is used to analyze and predict data on the rupiah exchange rate against the US dollar with inflation as an exogenous variabel. The exchange rate has an residual variance that is not constant so that the GARCH model is used to overcome the problem of heteroscedasticity. The results of this research show that forecasting the rupiah exchange rate against the US dollar fot the period January 2010 – December 2019 with the ARIMAX(0,1,1) – GARCH(1,0) model is the best model with a MAPE (1,1655) value which shows a low percentage compared to the ARIMAX model.]]>
<![CDATA[Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan ]]>
<![CDATA[Novilia Jao]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Nurtiti Sunusi]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11997
<![CDATA[Poisson regression analysis is a method used to analyze the relationship between predictor variables and response variables with a Poisson distribution. However, not all data have an orderly pattern, so the Poisson regression is not appropriate to use. To solve this problem, a multivariable Poisson nonparametric regression with a spline truncated estimator was used. In this research, the estimation parameters of multivariable Poisson nonparametric regression was applied to data of infant mortality rates in South Sulawesi in 2017. The infant mortality rate can be measured from the number of infant deaths under one year. The method of selecting the optimal knot point uses the Generalized Cross Validation (GCV) method. The best model is formed on a linear spline model with one knot point. Based on the estimation of the parameters formed, it shows that the variable number of babies with low birth weight (x1) and the number of infants who are exclusively breastfed (x3) significantly affect the number of infant deaths. Keywords: GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant Mortality.]]>
<![CDATA[Nilai Risiko Terkondisi pada Return Finansial Menggunakan Metode Copula Gumbel ]]>
<![CDATA[Najiha Alimatun]]>;
<![CDATA[Anisa Anisa]]>;
<![CDATA[Andi Kresna Jaya]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.12246
<![CDATA[The calculation of VaR is assumed normal distribution while the conditions in the real world distribution conditions of the return value depends on the market conditions that occurred at the time. Thus, this makes VaR estimates invalid which results in portfolio risk occurring greater than the predetermined risk. Therefore, In this study, the estimated risk value uses the Conditional Value at Risk (CVaR), which measures the expected value depending on what is the worst percentage of the risk loss, and using Copula Gumbel to model financial return in the investment data of PT. Telkomunikasi Indonesia tbk and PT. XL Axiata tbk. for the period March 11, 2019 to March 10, 2020. In this study, the CVaR estimation results for the 99% confidence level is 0.231, while for the VaR estimate it is 0.192. This indicates that risk value with CVaR estimate is better able to show higher risk than VaR.]]>
<![CDATA[Estimasi Parameter Model Regresi Data Panel Efek Tetap dengan Metode First Difference ]]>
<![CDATA[Asti Inayati Magfirah]]>;
<![CDATA[Raupong]]>;
<![CDATA[Georgina Maria Tinungki]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11278
<![CDATA[This study aims to estimate the regression parameters fixed effects panel data model using the first difference method on the influence of Life Expectancy, Average Length of School, and Per capita Expenditure on the Human Development Index of South Sulawesi in 2012 - 2018. The first difference method is used to obtain intercept differences in each district/city explaining the effect of regional differences. The first difference process results in autocorrelation of data so after the first difference is done the generalized least square method is used to estimate the parameters. The results show Life Expectancy, Average Length of School, and Per capita Expenditure has a significant influence on the Human Development Index of South Sulawesi in 2012 - 2018 simultaneously or partially.]]>
