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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Contemporary Mathematics and Applications (ConMathA) ESTIMASI: Journal of Statistics and Its Application
Andi Kresna Jaya
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Transportation Other

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Journal : ESTIMASI: Journal of Statistics and Its Application

 1 
Analisis Peluang Steady State Pada Kasus Covid-19 di Indonesia Menggunakan Rantai Markov Ika Pratiwi Haya; Andi Kresna Jaya; Nurtiti Sunusi
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24802

Abstract

Covid-19 in Indonesia began to be recorded on March 2, 2020 with the number of positive patient cases as many as 2 people with the passage of time Covid-19 cases in Indonesia are always increasing. To see the development of Covid-19 cases in the future period, the opportunity for the number of Covid-19 cases can be used using the Markov chain. The Markov chain method is carried out using a transition probability matrix which is seen from the number of additions to positive Covid-19 patients in a steady state or a situation for a long period of time. Based on the results of the range of additions to the number of positive cases of Covid-19, 6 states were used. Furthermore, the calculation of the Markov Chain in the stationary state of Covid-19 cases in Indonesia after 328 days or 11 months obtained the probability of each state, namely state 1 of 0.0005, state 2 of 0.0069, state 3 of 0.1707, state 4 of 0.1462, state 5 of 0.1884 , and state 6 is 0.4873. Prediction of the addition of positive Covid-19 patients obtained results as many as 2058 patients in state 5 for July 1, 2022 with actual data as many as 2049 patients.
Pemodelan Geographically Weighted Logistic Regression dengan Metode Ridge Reski Amalah; Andi Kresna Jaya; Nasrah Sirajang
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.12250

Abstract

One of the goals of national development is to reduce poverty. Poverty is included in the phenomenon of spatial heterogeneity because it can be shown by the varying economic conditions in each region. The statistical modeling method developed for data analysis takes into account regional factors namely Geographical Weighted Logistic Regression (GWLR). The parameter estimator of the GWLR semiparametric model used in this study was obtained using the Maximum Likelihood Estimation method. In GWLR, the assumption that must be fulfilled is the absence of multicollinearity. One method for dealing with multicollinearity is ridge regression involving the addition of a constant bias . The results obtained were the MSE value of the parameter estimator with the ridge method (707.77) smaller than the GWLR model before using the ridge (715.88). This shows that the ridge method is more effective if there are multicollinearity problems.
Estimation of Earthquake Intensity Function as a Form of Nonhomogenic Poisson Process Nur Fuadil Maqnun Wahab; Andi Kresna Jaya; Nurtit Sunusi
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.18007

Abstract

Earthquake is a natural phenomenon that is random in nature because its occurrence depends on time so that earthquakes are seen as a Nonhomogeneous Poisson Process. In this study, the Nonhomogeneous Poisson process was applied to estimate the number of earthquakes on the island of Sulawesi. The data used in this study is the occurrence of earthquakes on Sulawesi Island from January 2018 to December 2020 sourced from the Meteorology, Climatology and Geophysics Agency (BMKG) Region IV Makassar. The results of this study indicate that earthquakes that occur from one month to the next do not affect each other other than that the value of the intensity of the earthquake in each interval (month) is not the same, so that the estimated incidence of earthquakes on the island of Sulawesi with a strength of more than 5.0 SR is obtained. on 1 to 8 July 2021 is about 14 earthquakes with a standard deviation of about 3 times and the probability of an earthquake is 0.10537.
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.
Perbandingan Analisis Komponen Utama Robust Minimum Covarian Determinant dengan Least Trimmed Square pada Data Produk Domestik Regional Bruto Amni, Wa Ode Sitti Amni; Jaya, Andi Kresna; Ilyas, Nirwan
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.v5i2.32283

Abstract

Regression analysis is a method to examine the relationship between variables and determine their influence. However, the problem of multicollinearity often arises in linear regression analysis and can cause interpretation problems. To handle multicollinearity, Principal Component Analysis (PCA) is used. However, this method has a weakness when the data contains outliers. Therefore, it was developed into robust PCA using the Minimum Covariance Determinant (MCD) method and the Least Trimmed Square (LTS) estimation method. This study uses Gross Regional Domestic Product data in Indonesia in 2020, which has problems with multicollinearity and outliers. This data is modeled using two robust PCA methods, namely MCD and LTS. The robust PCA model with MCD has an adjusted value of 87.87% and an MSE value of 0.0700. However, in the robust PCA regression model with LTS, the adjusted value is 98.93% and the MSE value is 0.0325. Thus, the effective method in handling multicollinearity and outliers is the LTS method because it shows better results.
Pemodelan Regresi Zero Inflated Negative Binomial pada Data yang Mengalami Overdispersi Fajri, Ainul; Jaya, Andi Kresna; Talangko, La Podje
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.v6i1.21037

Abstract

Poisson regression is a nonlinear regression model with the response variables in the form of discrete data and Poisson distribution. Data analysis using Poisson regression must meet assumptions such as the variance value and the average value of the response variables have the same value. However, in its application, overdispersion often occurs, namely the variance value is greater than the average value. Overdispersion in Poisson regression can occur because of the number of zero observations on the response variable. Data with zero excess and overdispersion are more suitable for using ZINB regression. The ZINB regression model is a model formed from the mixed distribution of the Poisson gamma. The ZINB regression model parameters were estimated using the MLE method with the EM algorithm. This study was applied to data on the number of neonatal deaths in Makassar City in 2018. The results of testing the ZINB regression model parameters showed that the predictor variable that had a partially significant effect was the number of newborns with low birth weight.
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 Amni, Wa Ode Sitti Amni Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Azis, Moh. Ivan Fajri, Ainul Haryanto, Loeky Herdiani, Erna Tri Ika Pratiwi Haya Jusmawati Massalesse Mahie, Andi Galsan Marsya Anggun Prisila Najamuddin, Fadhila Febriyanti Nasrah Sirajang Nirwan Ilyas Niswar Aliansa Nur Erawati Nur Fuadil Maqnun Wahab Nurtit Sunusi NURTITI SUNUSI Reski Amalah Sahriman, Sitti Sunarti Sunarti Sutrianah Burhan Talangko, La Podje Yusuf, Anisa Haura Salsa Fatih