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ESTIMASI: Journal of Statistics and Its Application
Published by Universitas Hasanuddin
ISSN : 2721379X     EISSN : 27213803     DOI : http://dx.doi.org/10.20956/ejsa
Core Subject : Education,
Mathematics
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.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 107 Documents
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Metode Geographically Weighted Lasso dalam Pemodelan Tingkat Pengangguran Terbuka di Sulawesi Selatan Isnaini, Wulan Maulia; Aswi, Aswi; Sudarmin, Sudarmin
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.v6i1.30863

Abstract

The Open Unemployment Rate (TPT) in South Sulawesi which reached 6.07% in 2020 has an impact on the economy and welfare levels. TPT data in South Sulawesi has spatial diversity. To overcome spatial diversity in data analysis, the Geographically Weighted Regression (GWR) method can be used. However, GWR is less than optimal if multicollinearity occurs, so the Geographically Weighted Lasso (GWL) method is more appropriate. Research related to GWL on TPT in South Sulawesi has not been conducted. This study aims to obtain a GWL model with a spatial weighting matrix using a fixed exponential kernel weighting function and identify factors that influence TPT. The data used are TPT, population growth rate, literacy rate, illiteracy rate, average length of schooling, job vacancies, and job seekers. The results of the study showed that the factors influencing TPT were population growth rate, illiteracy rate, average length of schooling, and job vacancies in several districts/cities with an R2 value of 89.4%.
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.
Nigerian Population Growth Modelling and Forecasting using Univariate Time series model Taiwo, Abass Ishola; Titilola, A A; Olatayo, Timothy Olabisi; Lasisi, Taiwo Abideen
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.v6i1.34349

Abstract

The Nigerian population is growing rapidly, and this poses significant challenge to human existence. To have an insight, Autoregressive Integrated Moving Average (ARIMA) model was used to model and predict population growth. The results showed a yearly population mean of 99,611,692 with a standard deviation of 53,188,740. After estimation, the ARIMA(3,2,1) model was chosen for having lowest Akaike and Schwarz information criterion with the adequacy of the model attained using the Ljung-Box Statistic Autocorrelation and partial autocorrelation functions of the residuals. Coefficient and adjusted coefficient of determinations showed the model has a strong predictive accuracy, with the forecast indicating a continuous population growth increase of over 5 million annually. Conclusively, Nigerian government must plan how to curtail this explosive growth expected at over 418 million by 2050.
Pemodelan Regresi Spasial pada Tingkat Kemiskinan di Pulau Sulawesi Said, Baharuddin; Agusrawati, Agusrawati; Laome, Lilis
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.v6i1.40494

Abstract

In regression analysis, the independence assumption of the error terms is often violated when working with spatial data. The 2023 poverty incidence data across regencies/municipalities on Sulawesi Island indicate the presence of spatial autocorrelation. This study aims to compare the performance of classical regression, spatial autoregressive model (SAR), and spatial error model (SEM) in modeling poverty incidence on the island. The regency/municipality-level data used in the study is secondary data published by BPS-Statistics Indonesia. The findings reveal that the SEM model provides more accurate parameter estimates compared to classical regression and SAR model. Factors that have a significant influence on the poverty incidence (Y) in a regency/municipality are life expectancy (X1), expenditure per capita (X2), and the error terms for the nearest neighboring regions (λ).
Pendekatan Zero-Inflated Poisson Inverse Gaussian dalam Pemodelan Kasus Malaria di Puskesmas Kota Makassar Nurhidayah, Fauziah; Raupong, Raupong; Angriany, A.Muthiah Nur
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: 10.70561/ejsa.v6i1.43164

Abstract

Poisson regression is one of the approaches used to model count data. However, this method has an assumption of equidispersion that is not always met in actual data. One problem that often arises is overdispersion, especially when there are excess zeros in the dependent variable. The Mixed Poisson method, namely Zero-Inflated Poisson Inverse Gaussian (ZIPIG) regression is one approach that can be used when there is overdispersion in the data.  Parameter estimation in the ZIPIG model is done using the Maximum Likelihood Estimation (MLE) method through Fisher Scoring Algorithm iterations. This study discusses how ZIPIG modeling is used to identify factors that influence the number of malaria cases in Makassar City Health Center in 2021. The results of the analysis show that the independent variables that have a significant effect on the number of malaria cases are the number of family heads with access to proper sanitation facilities (X1) and the presence of public places that meet health requirements  (X2).
Model Robust Geographically Weighted Regression pada Data Kemiskinan di Sulawesi Selatan Tahun 2019 Rahman, Aqilah Salsabila; Tinungki, Georgina Maria; Herdiani, Erna Tri
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.v6i2.18046

Abstract

Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform analysis by assigning weights based on the geographical distance of each observation location and the assumption of having spatial heterogenity. The result of this analysis is an equation model whose parameter values apply only to each observation location and are different from other observation locations. However, when there are outliers at the observation location, a more robust estimation method is needed. One of the robust methods that can be applied to the GWR model is the Least Absolute Deviation method. In this study, model estimation was carried out on the factors that affect poverty in South Sulawesi in 2019 using Robust Geographically Weighted Regression (RGWR) with the Least Absolute Deviation (LAD) method. Determination of weighting is done by using the adaptive kernel bisquare weighting function. The results obtained are RGWR models which are different and apply only to each district/city in South Sulawesi. In addition, it was also found that the RGWR model with the LAD method was the best model for data that experienced spatial heterogenity and contained outliers.
Pemodelan Regresi Seemingly Unrelated Menggunakan Metode Maximum Likelihood pada Data Panel Hikmah, Nurul; Raupong, Raupong; Sirajang, Nasrah
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.v6i2.34463

Abstract

This study aims to model and predict the Human Development Index (HDI) values in South Sulawesi Province for the period 2014–2022 using a multiple linear regression approach with the Maximum Likelihood Estimation (MLE) method. Multiple linear regression analysis often encounters multicollinearity issues among independent variables; therefore, Principal Component Analysis (PCA) is employed as a dimensionality reduction technique to eliminate correlations among explanatory variables. In addition, due to the potential correlation of residuals among equations in a multivariate model, the Seemingly Unrelated Regression (SUR) approach is used, which is also estimated using the MLE method. The data utilized in this study is panel data, which offers advantages in obtaining more comprehensive and accurate information regarding the relationships between the analyzed variables. The estimation results of the SUR model indicate that variables such as Life Expectancy (UHH), Mean Years of Schooling (RLS), Expected Years of Schooling (HLS), and Adjusted Per Capita Expenditure have a significant influence on HDI across all districts/cities in South Sulawesi. One of the estimated equations from the SUR model is y22t=81.44+0.670KU122 which illustrates the relationship between the principal component and HDI in a specific region.
Analyzing Factors that Affect the Blitar Society Religiosity as the Impact of Wayang Wali Shows Based on Structural Equation Modelling Approaches Sari, Adma Novita; Widyangga, Pressylia Aluisina Putri; Romadhoni, M. Suma Firman; Yusuf, Bima Sakti Putra; Mardianto, M. Fariz Fadillah
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.v6i2.35850

Abstract

This research analyzes the impact of Wayang Wali performances on the religiosity of the Blitar society. Data was collected through a survey with stratified random sampling technique to 367 people in Blitar. The study employs two approaches: Confirmatory Factor Analysis (CFA) and Structural Equation Modeling-Partial Least Squares (SEM-PLS). CFA analysis show that all indicators for the five latent variables have shown significant Z-values, exceeding the threshold of 1.96. This confirms that the indicators are reliable measures for their respective latent constructs. Given this significance, the model demonstrates robust construct validity, ensuring that each latent variable is accurately represented by its indicators. From inner and outer models analysis, show that family environment, personality, social piety, and transpersonal psychology have an effect on the religiosity of the Blitar community because the t-statistical value > t table or 1.96 and the P-Value is 0.000 < 0.05. Through this research, Blitar government can enhance community religiosity, preserving cultural heritage and strengthening religious and moral values.
Penggunaan Metode Copula Gaussian untuk Menentukan Nilai Value at Risk Investasi Saham pada Bank BCA dan Bank BRI Palungan, Kevin Ekarinaldo; Kalondeng, Anisa; Ilyas, Nirwan
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.v6i2.35960

Abstract

Investment is capital for one or more assets over a long period of time to obtain profits. Besides being able to provide profits, stock investment also contains an element of risk. Therefore, risk measurement needs to be done so that the risk is within a controlled level so as to reduce the occurrence of investment losses. This study uses the Gaussian Copula to calculate Value at Risk on the closing price data of PT. Bank Central Asia Tbk and PT. Bank Rakyat Indonesia Tbk for the period January 02, 2020 to December 30, 2022. For the Kendall's correlation value τ=0.3307 produces a Pearson correlation value of ρ=0.4965 which is also used as an estimate of the Copula Gaussian parameter. The results of the VaR calculation on a portfolio with a weight of 50% shares of PT Bank Central Asia Tbk and 50% shares of PT Bank Rakyat Indonesia Tbk average VaR at the 95% confidence level of -0.0269 means that if investors invest their funds by 50% in PT Bank Central Asia Tbk shares and 50% in PT Bank Rakyat Indonesia Tbk shares there is a risk that the maximum loss is 2.69% of the invested funds.
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.

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