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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Sahriman, Sitti
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Transportation Other

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Journal : ESTIMASI: Journal of Statistics and Its Application

 1 
Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017 Yusuf, Anisa Haura Salsa Fatih; Jaya, Andi Kresna; Sahriman, Sitti
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i1.22749

Abstract

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.
Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan Waibusi, Hendriete Tiur Marowi; Tinungki, Georgina Maria; Sahriman, Sitti
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.25520

Abstract

ABSTRACT Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers. Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.
Penerapan Metode Exhaustive Chi-Square Automatic Interaction Detection pada Klasifikasi Penderita Diabetes dan Non Diabetes Nurhidayatullah, Nurhidayatullah; Sahriman, Sitti; Nirwan, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.33079

Abstract

Classification is a process of grouping an object into a certain category. One of classification method is the Exhaustive Chi-Square Automatic Interaction Detection (CHAID). The Exhaustive CHAID method is a classification method for categorical data by forming a classification tree. The classification tree interprets predictor variables that have a significant effect on the response variable based on the chi-square test. The purpose of this study was to obtain classification results for diabetics and non-diabetics using the Exhaustive CHAID method. The response variable used is the blood sugar level and the predictor variables consist of systolic blood pressure, diastolic blood pressure, length of sleep, working style, level of knowledge about diabetes, abdominal circumference, hereditary history of diabetes, age, exercise habits, and body mass index. The classification results show that the factors that have a significant influence at the 5% level are a hereditary history of diabetes, abdominal circumference, level of knowledge about diabetes, and diastolic blood pressure. Apart from that, the accuracy value of the Exhaustive CHAID classification tree is quite good, namely 86% based on the confusion matrix.
Perbandingan Model Threshold Generalized utoregressive Conditional Heteroscedasticity dan Exponential Generalized Autoregressive Conditional eteroscedasticity pada Peramalan Curah Hujan Andrianingrum, Amalia; Sahriman, Sitti; Jaya, Andi Kresna
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.43100

Abstract

Rainfall plays an important role in life and is closely related to other weather elements. Rainfall data is used for various purposes, including flood and drought risk mitigation and water resource planning. Makassar City has significant rainfall variability and requires accurate forecasting to manage its negative impacts. This study aims to predict rainfall in Makassar City from January 2021 to May 2023. The methods used are Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The results showed that the ARMA (2,1)-GARCH (1,2) model had MAPE and RMSEP values ​​of 1.234 and 33.411, respectively. The ARMA (2,1)-TGARCH (2,1) model had MAPE and RMSEP values ​​of 1.330 and 29.357, respectively. The ARMA (2,1)-EGARCH (1,2) model has MAPE and RMSEP values ​​of 0.924 and 32.641, respectively. The smallest MAPE and RMSEP values ​​are in the ARMA (2,1)-EGARCH (1,2) model. Thus, the ARMA (2,1)-EGARCH (1,2) model was selected as the best or optimal model for rainfall forecasting in Makassar City.
Co-Authors Aidawayati, Aidawayati Amir Kamal Amir Andi Kresna Jaya Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Anwar, Andi M. B., Hasmawati Edy Saputra Rusdi Georgina Maria Tinungki Hisyam, M. Zaky Gozhi Jaya, A. Kresna Jusmawati Jusmawati, Jusmawati Jusmawati Massalesse Kasbawati Kasbawati Naimah Aris Nirwan Nirwan Nur Erawaty Nurhidayatullah Nurhidayatullah, Nurhidayatullah Putra, Guntur Dwi Ramadhan, Muh. Nur Bahri Randa, Eunike Laurine Rangkuti, Aidawayati Raupong, Raupong Sirajang, Nasrah Sri Astuti Thamrin Surianda, Sitti Aisyah Taufik, Muh. Ikbal Thoha, Syamsuddin Upa, Dewi Wahda, Wahda Wahyuni, Alfiana Waibusi, Hendriete Tiur Marowi Yulianti, Andi Sri Yusuf, Anisa Haura Salsa Fatih