cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Anna Islamiyati
Contact Email
jurnalestimasi@unhas.ac.id
Phone
-
Journal Mail Official
jurnalestimasi@unhas.ac.id
Editorial Address
Jl. Perintis Kemerdekaan Km. 10 Tamalanrea Makassar - Indonesia, 90245
Location
Kota makassar,
Sulawesi selatan
INDONESIA
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 119 Documents
 8   9   10   11   12 
Identifikasi Prediktor Jumlah Kasus Baru Tuberkolosis di Jawa Barat: Perbandingan Regresi Poisson, Binomial Negatif, dan Poisson terbobot Geografis Khalishah, Athayya Putri; Advani, Nadjma Maulidya; Syahputra, Fathur Rahman; Brilliant, Indira Ihnu; Setiawan, Ezra Putranda
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.34314

Abstract

Tuberculosis (TB) is still a serious problem for the world, including Indonesia as the third largest contributor of TB cases in the world. This study aims to analyze the factors that affect the number of new TB cases in West Java Province as the province with the most TB cases in Indonesia in 2022. The response variable used is the number of new TB cases in West Java Province in 2022, while the predictor variables used are population density, number of AIDS cases, poverty, and sanitation. Since the dependent variable comes from counting procedure, we conducted the analysis through three models, namely Poisson regression, negative binomial regression, and Geographically Weighted Poisson Regression (GWPR). We find that in the negative binomial method there was only one insignificant predictor variable, namely population density. Based on influential predictor variables, GWPR models in districts / cities in West Java can be separated into four groups. The best model to analyze the factors affecting new TB cases is the negative binomial regression model with an AIC of 487.76.
Perbandingan Peta Kendali Poisson Double Progressive Mean dan Peta Kendali u pada Produksi Roti di Pakbatteang Mandiri Ningsi, Parida Ayu; Sirajang, Nasrah
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.35636

Abstract

Pakbatteang Mandiri is a company that operates in the food industry by producing bread, but the bread production process shows that there are defects in the product so a control chart is needed to monitor the number of production defects. Control charts that are suitable for use for calculated data such as the number of production defects are control charts based on the Poisson distribution such as the u control chart and the Poisson Double Progressive Mean control chart. This research aims to obtain a comparison of the Poisson Double Progressive Mean control chart and the u control chart for bread production in Pakbatteang Mandiri. The results of this research show that the Poisson Double Progressive Mean control chart detects more observation points that are out of control when compared to the u control chart. Based on the relatively small ARL value, the performance of the Poisson Double Progressive Mean control chart is more sensitive in detecting out of control than the u control chart.
Model Generalized Poisson Regression (GPR) pada Faktor-Faktor yang Mempengaruhi Jumlah Kasus Stunting di Kabupaten Kupang, Provinsi Nusa Tenggara Timur (NTT) Jeharu, Bernadinus; Guntur, Robertus Dole; Ginting, Keristina Br.; Pahnael, Jusrry Rosalina
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44106

Abstract

Stunting is a condition of failure to thrive due to chronic malnutrition, recurrent infections, and poor sanitation. East Nusa Tenggara (NTT) Province is the highest contributor of stunting cases in Indonesia and Kupang Regency is also the third highest contributor of stunting cases in NTT Province. This study aims to identify factors that influence the number of stunting cases using the Generalized Poisson Regression (GPR) model which is able to overcome overdispersion in count data. Secondary data for 2023 was obtained from the Kupang District Health Office and BPS. Independent variables included LBW, complete basic immunization (IDL), exclusive breastfeeding, nutritional status of children under five, access to sanitation and safe drinking water, vitamin A administration, number of health centers, and health workers. The results of the analysis show that the percentage of IDL toddlers, the percentage of neighborhoods with access to safe drinking water, the number of infants receiving Vitamin A, exclusive breastfeeding, the number of health centers, and the number of community health workers have a significant effect on the number of stunting cases in Kupang district. These findings can inform the formulation of more effective health intervention policies in the region.
Pemilihan Model Regresi Logistik Ordinal Terbaik Menggunakan Metode Stepwise: (Studi Kasus: Data Indeks Prestasi Kumulatif Lulusan Program Sarjana FMIPA Unmul) Selsi, Selsi; Darnah, Darnah; Wahyuningsih, Sri
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44426

Abstract

Ordinal logistic regression is one of the statistical methods used to model response variables with two or more categories that have levels. This study aims to model the cumulative grade point average data of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University in 2023 using ordinal logistic regression. Estimation of ordinal logistic regression model parameters is done using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The best model selection was conducted using the stepwise method based on the smallest Akaike Information Criterion (AIC) value and significant predictor variables. The selection process started with six predictor variables, then gradually eliminated three predictor variables because they were not significant and did not reduce the AIC value. The stepwise stage stopped at the model with three significant predictor variables that had an AIC value of 349,22. The results showed that the factors that had a significant effect on the cumulative grade point average of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University based on the best ordinal logistic regression model were study program, age, and admission pathway.
Segmentasi Wilayah Jawa Timur Berdasarkan Ketersediaan Fasilitas dan Tenaga Kesehatan Kurniawan, Muhammad Erlangga; Ananta, Aditya Putra; Anugrah, Muhammad Cahya Raka; Damaliana, Aviolla Terza; Wara, Shindi Sheila May
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44767

Abstract

Health is a fundamental indicator in measuring societal well-being, where the equitable distribution of healthcare facilities and personnel plays a critical role. This study aims to segment regions in East Java Province based on the availability of healthcare facilities (community health centers, general/special hospitals, pharmacies, integrated health posts, and primary clinics) and healthcare personnel (doctors, midwives, nurses, pharmacists). The methods used include Principal Component Analysis (PCA) for dimensionality reduction, followed by K-Means and Agglomerative Hierarchical Clustering (AHC) algorithms using Average Linkage and Cosine Similarity. The analysis results show that AHC provides more optimal outcomes, with a silhouette score of 0.75, compared to K-Means which only achieved 0.51. The segmentation produced three main clusters: low (Pacitan, Ponorogo, Madura), medium (Bojonegoro, Jember, Banyuwangi), and high (Surabaya, Malang, Sidoarjo). These findings reveal disparities in the distribution of healthcare services in East Java and can serve as a foundation for more targeted policy formulation to improve equitable access to healthcare, particularly in underserved regions.
Perbandingan Performa Quadratic Discriminant Analysis Klasik dan Robust pada Data Hasil Principal Component Analysis untuk Klasifikasi Jenis Kaca Irwanda, M. Fatta Arya; Syafriandi, Syafriandi
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44982

Abstract

This study aims to compare the performance of classical Quadratic Discriminant Analysis (QDA) and Robust Quadratic Discriminant Analysis (RQDA) after applying dimensionality reduction using Principal Component Analysis (PCA) on the Glass Identification Dataset. The dataset consists of eight chemical composition variables used to classify glass types based on their elemental characteristics. Prior to classification, discriminant analysis assumptions were examined, multivariate outliers were identified, and PCA was applied to address multicollinearity and enhance data stability. The PCA results indicate that the eight original variables can be reduced to five principal components, which collectively explain 93.20% of the total data variability. Classification was then performed using classical QDA and RQDA, where the latter incorporates the Minimum Covariance Determinant (MCD) estimator to obtain robust estimates of the mean vector and covariance matrix. Model performance was evaluated using a confusion matrix and the Apparent Error Rate (APER). The results show that both QDA and RQDA achieve the same classification accuracy of 63.7%, corresponding to an APER of 36.3%. These findings suggest that the application of PCA contributes to stabilizing the data structure and reducing the influence of outliers, thereby diminishing the advantage of robust estimation in this case. Nevertheless, RQDA remains a valuable alternative for classification tasks involving datasets with strong outliers or significant deviations from multivariate normality.
Pemodelan Nilai Tukar Rupiah terhadap Dolar AS Menggunakan Ridge Regression dengan Koreksi Autokorelasi Prais Winsten Parapa, Anni Ivoni; Raupong, Raupong; Angriany, A.Muthiah Nur
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.45517

Abstract

Linear regression is a statistical method used to analyze the relationship between dependent and independent variables. Parameter estimation is generally obtained through the Ordinary Least Square (OLS) method, which produces unbiased and efficient estimates. However, the presence of multicollinearity and autocorrelation can render OLS estimates suboptimal. The Ridge Regression method combined with Prais Winsten correction can to produce more accurate parameter estimates than OLS. Unlike the subjective approach in determining the bias constant () through Ridge Trace, this study determines the optimal  value by minimizing the Mean Square Error (MSE). The results show that the Ridge Regression model with Prais Winsten autocorrelation correction has an Adjusted R² of 78% and a Root Mean Square Error (RMSE) of Rp364,389. Four independent variables are found to have a significant effect on the exchange rate of the Indonesian Rupiah against the United States Dollar (USD), namely money supply, interest rate, exports, and imports.
Perbandingan Performa MSGARCH, LSTM, dan Hybrid MSGARCH-LSTM pada Peramalan Data Deret Waktu yang Mengandung Heteroskedastisitas Freya, Wa Ode Rona; Sadik, Kusman; Susetyo, Budi
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.45934

Abstract

Volatility forecasting is crucial for estimating potential portfolio losses, particularly in cryptocurrency markets like Bitcoin, which exhibit high and irregular price fluctuations. Models from the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family, including Markov Switching GARCH (MSGARCH), are widely used to handle heteroscedastic data and capture regime changes. Meanwhile, Long Short-Term Memory (LSTM) is effective for modeling nonlinear and complex patterns in financial time series. This study proposes a hybrid MSGARCH-LSTM model by incorporating MSGARCH predictions as additional input to the LSTM. The model is evaluated using simulated data resembling Bitcoin's characteristics, with Heteroscedasticity Mean Absolute Error (HMAE) as the primary metric, and analyzed using ANOVA and Tukey's post-hoc test. The results identify four superior hybrid configurations, all of which significantly outperform the standalone MSGARCH and LSTM models. Based on the characteristics of Bitcoin data, the MSGARCH (2-regime with sged error distribution)-LSTM model is selected for empirical analysis. This model achieved an HMAE of 0.3197 and an HMSE of 0.2088, with accuracy improvements of 61.20% and 83.50% compared to the standalone MSGARCH model. These findings indicate that the hybrid MSGARCH-LSTM model improves volatility forecasting accuracy in highly volatile cryptocurrency markets.
Penggunaan Seleksi Fitur Query Expansion Ranking dan Genetic Algorithm-Support Vector Machine untuk Analisis Sentimen pada Aplikasi Perbankan Jenius Haksar, Haksar; Siswanto, Siswanto; Ilyas, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.47395

Abstract

Jenius is a digital banking product from BTPN launched in 2016. In 2021, various opinions emerged on Twitter regarding cases of lost customer funds, necessitating sentiment analysis to understand public perception. This study used the Support Vector Machine (SVM) method with two feature selection approaches: Query Expansion Ranking (QER) and Genetic Algorithm (GA). The data used were 2,008 manually labeled tweets. The results showed that the QER-SVM method produced an accuracy of 87.81%, while the GA-SVM achieved an accuracy of 88.31% with improvements in precision and F-measure. Thus, the combination of Genetic Algorithm and SVM was more effective in classifying sentiment towards the Jenius application on Twitter.

Page 12 of 12 | Total Record : 119

 8   9   10   11   12 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 7, No. 1, Januari, 2026 : Estimasi Vol. 6, No. 2, Juli, 2025 : Estimasi Vol. 6, No. 1, Januari, 2025 : Estimasi Vol. 5, No. 2, Juli, 2024 : Estimasi Vol. 5, No. 1, Januari, 2024 : Estimasi Vol. 4, No. 2, Juli, 2023 : Estimasi Vol. 4, No. 1, Januari, 2023 : Estimasi Vol. 3, No. 2, Juli, 2022 : Estimasi Vol. 3, No. 1, Januari, 2022 : Estimasi Vol. 2, No. 2, Juli, 2021 : Estimasi Vol. 2, No. 1, Januari, 2021 : Estimasi Vol. 1, No. 2, Juli, 2020 : Estimasi Vol. 1, No. 1, Januari, 2020 : Estimasi More Issue