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Muhammad Athoillah
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kampus II Universitas PGRI Adi Buana Surabaya Jl. Dukuh Menanggal XII, Surabaya 60234 Jawa Timur, Indonesia.
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INDONESIA
J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika
Published by Universitas PGRI Adi Buana Surabaya
ISSN : 20890028     EISSN : 26547511     DOI : https://doi.org/10.36456/jstat.vol16.no2
Core Subject : Economy, Science,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
This journal publishes scientific articles in the form of research results, case studies, or literature reviews on various aspects related to the field of statistics, scientific data and their applications. Such as Computing, Time Series, Multivariate, Data Mining, Biostatistics, Survival Analysis, Econometrics, Spatial Analysis, Actuarial, Quality Control, Bayesian Analysis, Development Research in Statistics, Natural Language Processing, Applied Mathematics, Applied Statistics. However, the editorial team does not rule out other topics in the fields of statistics and scientific data.
Arjuna Subject : -
Articles 278 Documents
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Spatial Analysis of Gross Enrollment Ratio in Higher Education Using the Geographically Weighted Regression Approach
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10792

Abstract

This study analyzes the factors influencing the Gross Enrollment Rate of Higher Education (GER-HE) in Indonesia by emphasizing regional differences that cannot be adequately captured by global Ordinary Least Squares (OLS) or panel data models. The analysis is based on secondary data for 2023 obtained from Badan Pusat Statistik (BPS) and the Ministry of Education, including Gross Regional Domestic Product (GRDP), education funding, lecturer–student ratio, number of students, per capita expenditure, total population, and the poverty depth index. To capture spatial heterogeneity in these relationships, the study applies Geographically Weighted Regression (GWR) with a Gaussian kernel weighting function. The results indicate that the GWR model explains 50.57% of the variation in GER-HE, reflecting improved model performance after accounting for spatial variation across regions and providing a better fit than the OLS regression. The effects of explanatory variables on GER-HE vary across provinces, allowing regions to be classified into five groups based on combinations of statistically significant factors, particularly the number of students, per capita expenditure, and the poverty depth index. These findings suggest that higher education policies should be tailored to the specific characteristics of each regional group to enhance GER-HE and reduce interprovincial disparities.
Frequency Data Modeling of Passenger Transport Auto Insurance Claims Using the New Poisson Mixed Weighted Lindley Distribution
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10820

Abstract

Vehicle insurance is an important instrument in risk management. However, claim frequency modeling often faces overdispersion issues, rendering the equidispersion assumption in Poisson distribution invalid. Alternative distributions such as Negative Binomial have been widely used to address this issue, but they still have limitations in capturing claim heterogeneity in some insurance data. This study applies the New Poisson Mixed Weighted Lindley (NPWL) distribution to passenger transport vehicle insurance claim frequency data in Indonesia sourced from PT. XYZ in the 2013 underwriting year. Parameter estimation was performed using the Maximum Likelihood approach, and model fit was evaluated using the Chi-Square test. The results show that the NPWL model provides a good fit to the data, with a Chi-Square test statistic value of 0.7341, which is smaller than the critical value. The parameter estimates obtained were  4.5919 and  0.8539, which resulted in a mean value of 0.3811 and a variance of 0.4033. The variance being greater than the mean indicates that NPWL is able to capture overdispersion more flexibly than the conventional Poisson model, making it more relevant for practical applications in setting vehicle insurance premiums.
A Dynamic Spatial Durbin Panel Model for Analyzing Poverty Rates in West Java Province
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10826

Abstract

Poverty is a complex socioeconomic issue influenced by various internal and external factors and remains a major concern in Indonesia. Based on data from the Statistics Indonesia (BPS), the national poverty rate in 2025 reached 8.57%, decreasing by 0.1% from the previous year. In West Java Province, although poverty shows a declining trend, it remains the second-highest in terms of the number of poor residents, totaling about 3.67 million people. This study analyzes the factors affecting poverty levels in West Java by considering spatial and temporal interregional effects. The data consist of the poverty percentages of districts and cities in West Java and their related variables from 2019 to 2024. The method used is a dynamic spatial panel regression model with a spatial durbin approach. The results reveal significant spatial and dynamic effects on poverty. GRDP per capita significantly influences poverty within a region, while the unemployment rate, GRDP per capita, and population growth rate in neighboring regions also have significant effects. The model explains 99.59% of the interregional variation in poverty. These findings highlight the importance of regional coordination and sustainable policies for poverty reduction, particularly through job creation, economic equity, and population growth control.
Univariate vs. Multivariate: Comparing Univariate Panel Data and Panel SUR Approaches in Modeling Stunting, Wasting, and Underweight in Indonesia
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10840

Abstract

Objective: To support Indonesia’s progress toward the SDG Zero Hunger target, this study compares the performance of univariate panel data regression against the Panel Seemingly Unrelated Regression (Panel SUR) model in analyzing stunting, wasting, and underweight (2007–2023). Method: Using a two-way Feasible Generalized Least Squares (FGLS) approach, the study simultaneously estimates the malnutrition system to account for cross-equation error correlations. Results: The results demonstrate that Panel SUR outperforms the univariate model by correcting the theoretical inconsistency of the Low Birth Weight (LBW) coefficient—switching it from a counterintuitive negative to a positive sign while significantly increasing the explanatory power for the wasting equation and achieving superior estimation efficiency as evidenced by a lower Mean Square Error (MSE). Implications: These methodological improvements are substantively critical; correcting the LBW sign ensures that policy interventions accurately prioritize prenatal nutrition as a determinant of acute malnutrition, avoiding misleading inferences common in isolated modeling approaches. Conclusion: Consequently, Panel SUR offers a more robust empirical framework for formulating integrated nutrition policies than traditional univariate methods.
Text Mining for Classifying Potentially Depressive Tweets on X Using IndoBERT
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10873

Abstract

Depression is a severe issue in Indonesia, where sufferers often do not seek professional help and prefer to express themselves through social media such as the X. This study uses a text mining approach to classify potential depression using a dataset of 5,000 Indonesian-language tweets from October 2024 to January 2025. The preprocessing steps involves case folding, cleaning, normalization, and stopword removal. The dataset was labeled into two classes: potentially depressive and normal, then divided into 80% training data and 20% test data. A pre-trained IndoBERT model was adjusted with a learning rate of 2e-05, batch size of 8, and epoch of 2 for this depression potential classification task. The evaluation results showed that the IndoBERT model performed well with an accuracy of 87%, precision of 87%, recall of 87%, and f1 score of 87%. However, the model’s performance affected by class imbalance, so it tended to be better at predicting the majority label (normal) than the minority label (depression). Therefore, rebalancing is recommended  to prevent similar occurrences. The IndoBERT model used in this study was initialized from an emotion classification model, manual labelling was conducted by researchers in collaboratoin with psychiatrists to ensure clinical relevance. Finally, the trained model was deployed into a web-based application using Streamlit. This application was created as a preliminary screening tool to assist psychiatrists, not as a diagnostic system.
Modeling Aggregate Losses for Third Party Liability Insurance Using the Panjer Recursive Method
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10886

Abstract

The increasing number of passenger vehicles in densely populated areas such as Jakarta, West Java, and Banten has increased the risk of financial losses due to accidents, theft, and vehicle damage. This study aims to model the aggregate loss distribution in Third Party Liability (TPL) insurance using the Panjer recursive method. The data used are real observations from PT. XYZ for passenger vehicles insured between IDR 125 million and IDR 200 million in the 2018 underwriting year. Claim frequency is modeled using a Poisson distribution, while claim severity follows a Pareto Type II distribution. Model parameters are estimated using the Maximum Likelihood Estimation (MLE) method, and goodness-of-fit is evaluated using the Chi-square and Kolmogorov–Smirnov tests. The results show that the aggregate loss distribution can be effectively constructed using the Panjer recursive method. A dominant discrete probability mass occurs at zero aggregate loss with a probability of 0.992664, while the continuous component covers positive losses up to IDR 23,400,000. This result indicates that TPL claims in the observed portfolio are extremely sparse, which has important implications for premium pricing and risk management in motor vehicle insurance.
Complience Evaluation Based on National Fire Safety Standards and QSPM Strategic Priority Analysis
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 2 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no2.a10994

Abstract

Fire is a critical disaster with significant impacts on human safety and building functionality. This study aims to assess the preparedness of PT. The research employs a sequential exploratory mixed methods approach, integrated with SWOT analysis and the Quantitative Strategic Planning Matrix (QSPM). Primary data were gathered through observations, interviews, and regulatory-based checklists. Results indicate a grand average compliance index of 85.9%, categorized as "Good". However, SWOT analysis reveals vulnerabilities in human resource competency and passive infrastructure. The QSPM results prioritize Human Resource Capacity Building & Technical Certification (TAS 2.65) as the most strategic intervention to address residual risks in high-risk industrial environments.
Generalized Poisson Regression Modeling Using Fisher-Scoring Optimization for Cases of Malnutrition in Toddlers in Central Java
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol19.no1.a11250

Abstract

Malnutrition among infants and toddlers accounts for 45% of global child deaths, making accurate risk modeling essential. In Central Java, data on malnutrition cases show overdispersion (ϕ = 162.58), rendering the Poisson Regression (PR) model invalid. This study applied Generalized Poisson Regression (GPR) with Fisher-Scoring optimization to five predictor variables: complete basic immunization, active community health posts (posyandu), neonatal visits, iron-folic acid tablet (TTD) consumption by pregnant women, and living in poverty. The evaluation results show that GPR is superior with an AIC value of 505.68 and a Pearson Pseudo-R² of 0.1784. Based on the modeling results, iron tablet consumption and the number of poor residents have a significant effect, while active Posyandu does not. Furthermore, the categorical variable for basic immunization and the variable for neonatal visits showed anomalous results and were therefore eliminated to maintain model stability. This study demonstrates that GPR provides more reliable estimates to support targeted health intervention and poverty alleviation policies in Central Java.
Implementation of Robust Mixed Geographically and Temporally Weighted Regression for Crime Rate Prediction in East Java
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol19.no1.a11261

Abstract

The total number of crimes in East Java Province reached 60,102 cases in 2024, indicating spatial and temporal variation in crime patterns. These data characteristics are heterogeneous and contain several extreme values (outliers) that may affect the stability of standard regression model estimates. This study applies the Robust Mixed Geographically and Temporally Weighted Regression (RMGTWR) model to examine spatiotemporal crime patterns in East Java during the 2020–2024 period and to evaluate the model’s ability to handle outliers in heterogeneous data. The evaluation results show that the RMGTWR model produced an  of 0.84 and an MSE of 1912.247. This robust approach provides more stable parameter estimates and reduces the influence of the 14 identified outlier observations compared with the non-robust model. The findings indicate that the Human Development Index (HDI) and the Gini Ratio have significant global effects, while the Police Ratio variable shows varying local effects. These results confirm that the RMGTWR approach accommodates data heterogeneity while mitigating distortions caused by outliers in crime analysis in East Java.
Analysis of Female Labor Force Participation in Java Island Using Multiscale Geographically Weighted Regression with an Adaptive Bisquare Kernel
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 19 No 1 (2026): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol19.no1.a11265

Abstract

Female labor force participation is an important indicator in labor development and gender equality. However, the Female Labor Force Participation Rate (FLFPR) in Java Island still shows disparities across regions, indicating the presence of spatial influences. This study aims to analyze the socio-economic factors affecting the FLFPR in 119 regencies/cities on Java Island in 2024 using the Multiscale Geographically Weighted Regression (MGWR) method with an adaptive bisquare kernel implemented in Python. The data used in this study were obtained from Statistics Indonesia (Badan Pusat Statistik) in 2024. The explanatory variables include the Regency/City Minimum Wage, number of poor population, Gender Inequality Index, Gross Regional Domestic Product, percentage of women in parliament, number of women managing households, and the Gender Development Index. The contribution of this research lies in the specific application of MGWR with an adaptive bisquare kernel for the Java Island region, which allows parameter estimates to vary across locations. The results indicate significant spatial heterogeneity. The minimum wage variable is the most consistently significant factor with a negative effect in most regions. These findings imply the need for region-specific employment and women’s empowerment policies rather than uniform policies across Java Island.

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